TY - JOUR AU - Wei, Xindi AU - Zhuang, Longfei AU - Li, Yuan AU - Shi, Junyu AU - Yang, Yijie AU - Lai, Hongchang AU - Liu, Beilei PY - 2025/3/21 TI - Edentulousness and the Likelihood of Becoming a Centenarian: Longitudinal Observational Study JO - JMIR Aging SP - e68444 VL - 8 KW - public health KW - edentulous KW - oral-systemic disease KW - epidemiology KW - cohort studies N2 - Background: In recent decades, the global life expectancy has risen notably to approximately 73.5 years worldwide, coinciding with a rapid growth in the older adult population, which presents a significant public health challenge in promoting healthy aging and longevity. Objective: This study aimed to prospectively investigate the link between edentulousness and the likelihood of reaching centenarian status among individuals aged 80 years and older. Methods: Data from the Chinese Longitudinal Healthy Longevity Survey were analyzed. Logistic regression models were used to assess the relationship between edentulousness and the likelihood of becoming a centenarian. Demographic characteristics, lifestyle habits, and disease histories were adjusted as confounding factors. Several sensitivity analyses, including propensity score matching and 2-year lag analyses, were conducted to further assess the association between edentulousness and the likelihood of becoming a centenarian. The correlation between the number of natural teeth as a continuous variable and the likelihood of becoming a centenarian was evaluated as well. Results: The study included 4239 participants aged 80-100 years. After adjusting for all covariates, the likelihood for becoming a centenarian increased in the nonedentulous group compared to the edentulous group (odds ratio [OR] 1.384, 95% CI 1.093?1.751). The relationship persisted after propensity score matching analysis (OR 1.272, 95% CI 1.037?1.561). The association remained statistically significant after excluding participants with a follow-up duration of less than 2 years (OR 1.522, 95% CI 1.083?2.140; P=.02). Furthermore, a significant positive association between the number of natural teeth and the likelihood of becoming a centenarian was found after adjusting for all covariates (OR 1.022, 95% CI 1.002?1.042; P=.03), which aligned with the main results of the study. Conclusions: The findings revealed that the presence of natural teeth was linked to an increased probability of becoming a centenarian, underscoring the importance of maintaining oral health even in advanced age. UR - https://aging.jmir.org/2025/1/e68444 UR - http://dx.doi.org/10.2196/68444 ID - info:doi/10.2196/68444 ER - TY - JOUR AU - Paek, Hunki AU - Fortinsky, H. Richard AU - Lee, Kyeryoung AU - Huang, Liang-Chin AU - Maghaydah, S. Yazeed AU - Kuchel, A. George AU - Wang, Xiaoyan PY - 2025/2/25 TI - Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study JO - JMIR Aging SP - e65221 VL - 8 KW - dementia KW - memory loss KW - memory KW - cognitive KW - Alzheimer disease KW - natural language processing KW - NLP KW - deep learning KW - machine learning KW - real-world insights KW - electronic health records KW - EHR KW - cohort KW - diagnosis KW - diagnostic KW - trajectory KW - pattern KW - prognosis KW - geriatric KW - older adults KW - aging N2 - Background: Understanding the dementia disease trajectory and clinical practice patterns in outpatient settings is vital for effective management. Knowledge about the path from initial memory loss complaints to dementia diagnosis remains limited. Objective: This study aims to (1) determine the time intervals between initial memory loss complaints and dementia diagnosis in outpatient care, (2) assess the proportion of patients receiving cognition-enhancing medication prior to dementia diagnosis, and (3) identify patient and provider characteristics that influence the time between memory complaints and diagnosis and the prescription of cognition-enhancing medication. Methods: This retrospective cohort study used a large outpatient electronic health record (EHR) database from the University of Connecticut Health Center, covering 2010?2018, with a cohort of 581 outpatients. We used a customized deep learning?based natural language processing (NLP) pipeline to extract clinical information from EHR data, focusing on cognition-related symptoms, primary caregiver relation, and medication usage. We applied descriptive statistics, linear, and logistic regression for analysis. Results: The NLP pipeline showed precision, recall, and F1-scores of 0.97, 0.93, and 0.95, respectively. The median time from the first memory loss complaint to dementia diagnosis was 342 (IQR 200-675) days. Factors such as the location of initial complaints and diagnosis and primary caregiver relationships significantly affected this interval. Around 25.1% (146/581) of patients were prescribed cognition-enhancing medication before diagnosis, with the number of complaints influencing medication usage. Conclusions: Our NLP-guided analysis provided insights into the clinical pathways from memory complaints to dementia diagnosis and medication practices, which can enhance patient care and decision-making in outpatient settings. UR - https://aging.jmir.org/2025/1/e65221 UR - http://dx.doi.org/10.2196/65221 ID - info:doi/10.2196/65221 ER - TY - JOUR AU - Mitsutake, Seigo AU - Ishizaki, Tatsuro AU - Yano, Shohei AU - Hirata, Takumi AU - Ito, Kae AU - Furuta, Ko AU - Shimazaki, Yoshitomo AU - Ito, Hideki AU - Mudge, Alison AU - Toba, Kenji PY - 2025/2/6 TI - Predictive Validity of Hospital-Associated Complications of Older People Identified Using Diagnosis Procedure Combination Data From an Acute Care Hospital in Japan: Observational Study JO - JMIR Aging SP - e68267 VL - 8 KW - delirium KW - functional decline KW - Japan KW - older adult KW - routinely collected health data KW - elder KW - hospital complication KW - HAC-OP KW - incontinence KW - pressure injury KW - inpatient care KW - diagnosis procedure combination KW - predictive validity KW - hospital length of stay KW - administrative data KW - acute care KW - index hospitalization KW - diagnostic code KW - linear regression KW - logistic regression KW - long-term care KW - retrospective cohort KW - observational study KW - patient care KW - gerontology KW - hospital care KW - patient complication N2 - Background: A composite outcome of hospital-associated complications of older people (HAC-OP; comprising functional decline, delirium, incontinence, falls, and pressure injuries) has been proposed as an outcome measure reflecting quality of acute hospital care. Estimating HAC-OP from routinely collected administrative data could facilitate the rapid and standardized evaluation of interventions in the clinical setting, thereby supporting the development, improvement, and wider implementation of effective interventions. Objective: This study aimed to create a Diagnosis Procedure Combination (DPC) data version of the HAC-OP measure (HAC-OP-DPC) and demonstrate its predictive validity by assessing its associations with hospital length of stay (LOS) and discharge destination. Methods: This retrospective cohort study acquired DPC data (routinely collected administrative data) from a general acute care hospital in Tokyo, Japan. We included data from index hospitalizations for patients aged ?65 years hospitalized for ?3 days and discharged between July 2016 and March 2021. HAC-OP-DPC were identified using diagnostic codes for functional decline, incontinence, delirium, pressure injury, and falls occurring during the index hospitalization. Generalized linear regression models were used to examine the associations between HAC-OP-DPC and LOS, and logistic regression models were used to examine the associations between HAC-OP-DPC and discharge to other hospitals and long-term care facilities (LTCFs). Results: Among 15,278 patients, 3610 (23.6%) patients had coding evidence of one or more HAC-OP-DPC (1: 18.8% and ?2: 4.8%). Using ?no HAC-OP-DPC? as the reference category, the analysis showed a significant and graded association with longer LOS (adjusted risk ratio for patients with one HAC-OP-DPC 1.29, 95% CI 1.25-1.33; adjusted risk ratio for ?2 HAC-OP-DPC 1.97, 95% CI 1.87-2.08), discharge to another hospital (adjusted odds ratio [AOR] for one HAC-OP-DPC 2.36, 95% CI 2.10-2.65; AOR for ?2 HAC-OP-DPC 6.96, 95% CI 5.81-8.35), and discharge to LTCFs (AOR for one HAC-OP-DPC 1.35, 95% CI 1.09-1.67; AOR for ?2 HAC-OP-DPC 1.68, 95% CI 1.18-2.39). Each individual HAC-OP was also significantly associated with longer LOS and discharge to another hospital, but only delirium was associated with discharge to LTCF. Conclusions: This study demonstrated the predictive validity of the HAC-OP-DPC measure for longer LOS and discharge to other hospitals and LTCFs. To attain a more robust understanding of these relationships, additional studies are needed to verify our findings in other hospitals and regions. The clinical implementation of HAC-OP-DPC, which is identified using routinely collected administrative data, could support the evaluation of integrated interventions aimed at optimizing inpatient care for older adults. UR - https://aging.jmir.org/2025/1/e68267 UR - http://dx.doi.org/10.2196/68267 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/68267 ER - TY - JOUR AU - Takura, Tomoyuki AU - Yokoi, Hiroyoshi AU - Honda, Asao PY - 2024/12/6 TI - Factors Influencing Drug Prescribing for Patients With Hospitalization History in Circulatory Disease?Patient Severity, Composite Adherence, and Physician-Patient Relationship: Retrospective Cohort Study JO - JMIR Aging SP - e59234 VL - 7 KW - medication adherence KW - drug prescription switch KW - generic drug KW - logistic model KW - long-term longitudinal study KW - patient severity KW - systolic blood pressure KW - serum creatinine KW - aging KW - big data N2 - Background: With countries promoting generic drug prescribing, their growth may plateau, warranting further investigation into the factors influencing this trend, including physician and patient perspectives. Additional strategies may be needed to maximize the switch to generic drugs while ensuring health care system sustainability, focusing on factors beyond mere low cost. Emphasizing affordability and clarifying other prescription considerations are essential. Objective: This study aimed to provide initial insights into how patient severity, composite adherence, and physician-patient relationships impact generic switching. Methods: This study used a long-term retrospective cohort design by analyzing data from a national health care database. The population included patients of all ages, primarily older adults, who required primary-to-tertiary preventive actions with a history of hospitalization for cardiovascular diseases (ICD-10 [International Statistical Classification of Diseases, Tenth Revision]) from April 2014 to March 2018 (4 years). We focused on switching to generic drugs, with temporal variations in clinical parameters as independent variables. Lifestyle factors (smoking and drinking) were also considered. Adherence was measured as a composite score comprising 11 elements. The physician-patient relationship was established based on the interval between physician change and prescription. Logistic regression analysis and propensity score matching were used, along with complementary analysis of physician-patient relationships, proportion of days covered, and adherence for a subset of the population. Results: The study included 48,456 patients with an average follow-up of 36.1 (SD 8.8) months. The mean age was 68.3?(SD 9.9)?years; BMI, 23.4?(SD?3.4)?kg/m2; systolic blood pressure, 131.2?(SD?15)?mm Hg; low-density lipoprotein cholesterol level, 116.6?(SD?29.3)?mg/dL; hemoglobin A1c (HbA1c), 5.9%?(SD?0.8%); and serum creatinine level, 0.9?(SD?0.8)?mg/dL. Logistic regression analysis revealed significant associations between generic switching and systolic blood pressure (odds ratio [OR] 0.996, 95% CI 0.993-0.999), serum creatinine levels (OR 0.837, 95% CI 0.729-0.962), glutamic oxaloacetic transaminase levels (OR 0.994, 95% CI 0.990-0.997), proportion of days covered score (OR 0.959, 95% CI 0.948-0.97), and adherence score (OR 0.910, 95% CI 0.875-0.947). In addition, generic drug rates increased with improvements in the HbA1c level band and smoking level (P<.01 and P<.001). The group with a superior physician-patient relationship after propensity score matching had a significantly higher rate of generic drug prescribing (51.6%, SD 15.2%) than the inferior relationship group (47.7%, SD17.7%; P<.001). Conclusions: Although physicians? understanding influences the choice of generic drugs, patient condition (severity) and adherence also impact this decision. For example, improved creatinine levels are associated with generic drug choice, while stronger physician-patient relationships correlate with higher rates of generic drug use. These findings may contribute to the appropriate prescription of pharmaceuticals if the policy diffusion of generic drugs begins to slow down. Thus, preventing serious illness while building trust may result in clinical benefits and positive socioeconomic outcomes. UR - https://aging.jmir.org/2024/1/e59234 UR - http://dx.doi.org/10.2196/59234 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59234 ER - TY - JOUR AU - Singh, Ankita AU - Chakraborty, Shayok AU - He, Zhe AU - Pang, Yuanying AU - Zhang, Shenghao AU - Subedi, Ronast AU - Lustria, Liza Mia AU - Charness, Neil AU - Boot, Walter PY - 2024/9/16 TI - Predicting Adherence to Computer-Based Cognitive Training Programs Among Older Adults: Study of Domain Adaptation and Deep Learning JO - JMIR Aging SP - e53793 VL - 7 KW - domain adaptation KW - adherence KW - cognitive training KW - deep neural networks KW - early detection of cognitive decline N2 - Background: Cognitive impairment and dementia pose a significant challenge to the aging population, impacting the well-being, quality of life, and autonomy of affected individuals. As the population ages, this will place enormous strain on health care and economic systems. While computerized cognitive training programs have demonstrated some promise in addressing cognitive decline, adherence to these interventions can be challenging. Objective: The objective of this study is to improve the accuracy of predicting adherence lapses to ultimately develop tailored adherence support systems to promote engagement with cognitive training among older adults. Methods: Data from 2 previously conducted cognitive training intervention studies were used to forecast adherence levels among older participants. Deep convolutional neural networks were used to leverage their feature learning capabilities and predict adherence patterns based on past behavior. Domain adaptation (DA) was used to address the challenge of limited training data for each participant, by using data from other participants with similar playing patterns. Time series data were converted into image format using Gramian angular fields, to facilitate clustering of participants during DA. To the best of our knowledge, this is the first effort to use DA techniques to predict older adults? daily adherence to cognitive training programs. Results: Our results demonstrated the promise and potential of deep neural networks and DA for predicting adherence lapses. In all 3 studies, using 2 independent datasets, DA consistently produced the best accuracy values. Conclusions: Our findings highlight that deep learning and DA techniques can aid in the development of adherence support systems for computerized cognitive training, as well as for other interventions aimed at improving health, cognition, and well-being. These techniques can improve engagement and maximize the benefits of such interventions, ultimately enhancing the quality of life of individuals at risk for cognitive impairments. This research informs the development of more effective interventions, benefiting individuals and society by improving conditions associated with aging. UR - https://aging.jmir.org/2024/1/e53793 UR - http://dx.doi.org/10.2196/53793 ID - info:doi/10.2196/53793 ER - TY - JOUR AU - Anderson, Euan AU - Lennon, Marilyn AU - Kavanagh, Kimberley AU - Weir, Natalie AU - Kernaghan, David AU - Roper, Marc AU - Dunlop, Emma AU - Lapp, Linda PY - 2024/8/7 TI - Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review JO - Online J Public Health Inform SP - e57618 VL - 16 KW - telecare KW - telehealth KW - telemedicine KW - data analytics KW - predictive models KW - scoping review KW - predictive KW - predict KW - prediction KW - predictions KW - synthesis KW - review methods KW - review methodology KW - search KW - searches KW - searching KW - scoping KW - home N2 - Background: Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care. Objective: This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings. Methods: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O?Malley?s methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria. Results: In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth. Conclusions: All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested. UR - https://ojphi.jmir.org/2024/1/e57618 UR - http://dx.doi.org/10.2196/57618 UR - http://www.ncbi.nlm.nih.gov/pubmed/39110501 ID - info:doi/10.2196/57618 ER - TY - JOUR AU - Razjouyan, Javad AU - Orkaby, R. Ariela AU - Horstman, J. Molly AU - Goyal, Parag AU - Intrator, Orna AU - Naik, D. Aanand PY - 2024/6/27 TI - The Frailty Trajectory?s Additional Edge Over the Frailty Index: Retrospective Cohort Study of Veterans With Heart Failure JO - JMIR Aging SP - e56345 VL - 7 KW - gerontology KW - geriatric KW - geriatrics KW - older adult KW - older adults KW - elder KW - elderly KW - older person KW - older people KW - ageing KW - aging KW - frailty KW - frailty index KW - frailty trajectory KW - frail KW - weak KW - weakness KW - heart failure KW - HF KW - cardiovascular disease KW - CVD KW - congestive heart failure KW - CHF KW - myocardial infarction KW - MI KW - unstable angina KW - angina KW - cardiac arrest KW - atherosclerosis KW - cardiology KW - cardiac KW - cardiologist KW - cardiologists UR - https://aging.jmir.org/2024/1/e56345 UR - http://dx.doi.org/10.2196/56345 ID - info:doi/10.2196/56345 ER - TY - JOUR AU - Yang, Jingzhen AU - Alshaikh, Enas AU - Yu, Deyue AU - Kerwin, Thomas AU - Rundus, Christopher AU - Zhang, Fangda AU - Wrabel, G. Cameron AU - Perry, Landon AU - Lu, Zhong-Lin PY - 2024/6/26 TI - Visual Function and Driving Performance Under Different Lighting Conditions in Older Drivers: Preliminary Results From an Observational Study JO - JMIR Form Res SP - e58465 VL - 8 KW - nighttime driving KW - functional vision KW - driving simulation KW - older drivers KW - visual functions KW - photopic KW - mesopic KW - glare KW - driving simulator N2 - Background: Age-related vision changes significantly contribute to fatal crashes at night among older drivers. However, the effects of lighting conditions on age-related vision changes and associated driving performance remain unclear. Objective: This pilot study examined the associations between visual function and driving performance assessed by a high-fidelity driving simulator among drivers 60 and older across 3 lighting conditions: daytime (photopic), nighttime (mesopic), and nighttime with glare. Methods: Active drivers aged 60 years or older participated in visual function assessments and simulated driving on a high-fidelity driving simulator. Visual acuity (VA), contrast sensitivity function (CSF), and visual field map (VFM) were measured using quantitative VA, quantitative CSF, and quantitative VFM procedures under photopic and mesopic conditions. VA and CSF were also obtained in the presence of glare in the mesopic condition. Two summary metrics, the area under the log CSF (AULCSF) and volume under the surface of VFM (VUSVFM), quantified CSF and VFM. Driving performance measures (average speed, SD of speed [SDspeed], SD of lane position (SDLP), and reaction time) were assessed under daytime, nighttime, and nighttime with glare conditions. Pearson correlations determined the associations between visual function and driving performance across the 3 lighting conditions. Results: Of the 20 drivers included, the average age was 70.3 years; 55% were male. Poor photopic VA was significantly correlated with greater SDspeed (r=0.26; P<.001) and greater SDLP (r=0.31; P<.001). Poor photopic AULCSF was correlated with greater SDLP (r=?0.22; P=.01). Poor mesopic VUSFVM was significantly correlated with slower average speed (r=?0.24; P=.007), larger SDspeed (r=?0.19; P=.04), greater SDLP (r=?0.22; P=.007), and longer reaction times (r=?0.22; P=.04) while driving at night. For functional vision in the mesopic condition with glare, poor VA was significantly correlated with longer reaction times (r=0.21; P=.046) while driving at night with glare; poor AULCSF was significantly correlated with slower speed (r=?0.32; P<.001), greater SDLP (r=?0.26; P=.001) and longer reaction times (r=?0.2; P=.04) while driving at night with glare. No other significant correlations were observed between visual function and driving performance under the same lighting conditions. Conclusions: Visual functions differentially affect driving performance in different lighting conditions among older drivers, with more substantial impacts on driving during nighttime, especially in glare. Additional research with larger sample sizes is needed to confirm these results. UR - https://formative.jmir.org/2024/1/e58465 UR - http://dx.doi.org/10.2196/58465 UR - http://www.ncbi.nlm.nih.gov/pubmed/38922681 ID - info:doi/10.2196/58465 ER - TY - JOUR AU - Kim, Hoon Seung AU - Kim, Hyunkyu AU - Jeong, Hoon Sung AU - Park, Eun-Cheol PY - 2024/5/2 TI - Association of the Type of Public Pension With Mental Health Among South Korean Older Adults: Longitudinal Observational Study JO - JMIR Public Health Surveill SP - e49129 VL - 10 KW - depression KW - retirement KW - contributory public pension KW - low-income household KW - public health KW - mental health KW - data KW - big data KW - longitudinal data KW - low income N2 - Background: As income and health are closely related, retirement is considered undesirable for health. Many studies have shown the association between pension and health, but no research has considered the association between contribution-based public pensions or their types and health. Objective: This study investigates the association between the type of contributory public pension and depressive symptoms among older adults. Methods: We analyzed the data of 4541 older adults who participated in the South Korea Welfare Panel Study (2014-2020). Depressive symptoms were measured using the 11-item Center for Epidemiologic Studies Depression scale. Public pensions in South Korea are classified into specific corporate pensions and national pensions. For subgroup analyses, pensioners were categorized according to the amount of pension received and the proportion of public pension over gross income. Analyses using generalized estimating equations were conducted for longitudinal data. Results: Individuals receiving public pension, regardless of the pension type, demonstrated significantly decreased depressive symptoms (national pension: ?=?.734; P<.001; specific corporate pension: ?=?.775; P=.02). For both pension types, the higher the amount of benefits, the lower were the depression scores. However, this association was absent for those who received the smaller amount among the specific corporate pensioners. In low-income households, the decrease in the depressive symptoms based on the amount of public pension benefits was greater (fourth quartile of national pension: ?=?1.472; P<.001; second and third quartiles of specific corporate pension: ?=?3.646; P<.001). Conclusions: Our study shows that contributory public pension is significantly associated with lower depressive symptoms, and this association is prominent in low-income households. Thus, contributory public pensions may be good income sources for improving the mental health of older adults after retirement. UR - https://publichealth.jmir.org/2024/1/e49129 UR - http://dx.doi.org/10.2196/49129 UR - http://www.ncbi.nlm.nih.gov/pubmed/38696246 ID - info:doi/10.2196/49129 ER - TY - JOUR AU - Romano, D. Joseph AU - Truong, Van AU - Kumar, Rachit AU - Venkatesan, Mythreye AU - Graham, E. Britney AU - Hao, Yun AU - Matsumoto, Nick AU - Li, Xi AU - Wang, Zhiping AU - Ritchie, D. Marylyn AU - Shen, Li AU - Moore, H. Jason PY - 2024/4/18 TI - The Alzheimer?s Knowledge Base: A Knowledge Graph for Alzheimer Disease Research JO - J Med Internet Res SP - e46777 VL - 26 KW - Alzheimer disease KW - knowledge graph KW - knowledge base KW - artificial intelligence KW - drug repurposing KW - drug discovery KW - open source KW - Alzheimer KW - etiology KW - heterogeneous graph KW - therapeutic targets KW - machine learning KW - therapeutic discovery N2 - Background: As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease?s etiology and response to drugs. Objective: We designed the Alzheimer?s Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. Methods: We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. Results: AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. Conclusions: AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge. UR - https://www.jmir.org/2024/1/e46777 UR - http://dx.doi.org/10.2196/46777 UR - http://www.ncbi.nlm.nih.gov/pubmed/38635981 ID - info:doi/10.2196/46777 ER - TY - JOUR AU - Choi, Jung-Yeon AU - Yoo, Sooyoung AU - Song, Wongeun AU - Kim, Seok AU - Baek, Hyunyoung AU - Lee, Suh Jun AU - Yoon, Yoo-Seok AU - Yoon, Seonghae AU - Lee, Hae-Young AU - Kim, Kwang-il PY - 2023/11/13 TI - Development and Validation of a Prognostic Classification Model Predicting Postoperative Adverse Outcomes in Older Surgical Patients Using a Machine Learning Algorithm: Retrospective Observational Network Study JO - J Med Internet Res SP - e42259 VL - 25 KW - CDM KW - common data model KW - patient-level prediction KW - OHDSI KW - Observational Health Data Sciences and Informatics KW - postoperative outcome KW - postoperative KW - surgery KW - elderly KW - elder KW - predict KW - adverse event KW - adverse outcome KW - geriatric KW - older adult KW - ageing KW - model KW - algorithm N2 - Background: Older adults are at an increased risk of postoperative morbidity. Numerous risk stratification tools exist, but effort and manpower are required. Objective: This study aimed to develop a predictive model of postoperative adverse outcomes in older patients following general surgery with an open-source, patient-level prediction from the Observational Health Data Sciences and Informatics for internal and external validation. Methods: We used the Observational Medical Outcomes Partnership common data model and machine learning algorithms. The primary outcome was a composite of 90-day postoperative all-cause mortality and emergency department visits. Secondary outcomes were postoperative delirium, prolonged postoperative stay (?75th percentile), and prolonged hospital stay (?21 days). An 80% versus 20% split of the data from the Seoul National University Bundang Hospital (SNUBH) and Seoul National University Hospital (SNUH) common data model was used for model training and testing versus external validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) with a 95% CI. Results: Data from 27,197 (SNUBH) and 32,857 (SNUH) patients were analyzed. Compared to the random forest, Adaboost, and decision tree models, the least absolute shrinkage and selection operator logistic regression model showed good internal discriminative accuracy (internal AUC 0.723, 95% CI 0.701-0.744) and transportability (external AUC 0.703, 95% CI 0.692-0.714) for the primary outcome. The model also possessed good internal and external AUCs for postoperative delirium (internal AUC 0.754, 95% CI 0.713-0.794; external AUC 0.750, 95% CI 0.727-0.772), prolonged postoperative stay (internal AUC 0.813, 95% CI 0.800-0.825; external AUC 0.747, 95% CI 0.741-0.753), and prolonged hospital stay (internal AUC 0.770, 95% CI 0.749-0.792; external AUC 0.707, 95% CI 0.696-0.718). Compared with age or the Charlson comorbidity index, the model showed better prediction performance. Conclusions: The derived model shall assist clinicians and patients in understanding the individualized risks and benefits of surgery. UR - https://www.jmir.org/2023/1/e42259 UR - http://dx.doi.org/10.2196/42259 UR - http://www.ncbi.nlm.nih.gov/pubmed/37955965 ID - info:doi/10.2196/42259 ER - TY - JOUR AU - Velazquez-Diaz, Daniel AU - Arco, E. Juan AU - Ortiz, Andres AU - Pérez-Cabezas, Verónica AU - Lucena-Anton, David AU - Moral-Munoz, A. Jose AU - Galán-Mercant, Alejandro PY - 2023/10/20 TI - Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review JO - J Med Internet Res SP - e47346 VL - 25 KW - frail older adult KW - identification KW - diagnosis KW - artificial intelligence KW - review KW - frailty KW - older adults KW - aging KW - biological variability KW - detection KW - accuracy KW - sensitivity KW - screening KW - tool N2 - Background: Frailty syndrome (FS) is one of the most common noncommunicable diseases, which is associated with lower physical and mental capacities in older adults. FS diagnosis is mostly focused on biological variables; however, it is likely that this diagnosis could fail owing to the high biological variability in this syndrome. Therefore, artificial intelligence (AI) could be a potential strategy to identify and diagnose this complex and multifactorial geriatric syndrome. Objective: The objective of this scoping review was to analyze the existing scientific evidence on the use of AI for the identification and diagnosis of FS in older adults, as well as to identify which model provides enhanced accuracy, sensitivity, specificity, and area under the curve (AUC). Methods: A search was conducted using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines on various databases: PubMed, Web of Science, Scopus, and Google Scholar. The search strategy followed Population/Problem, Intervention, Comparison, and Outcome (PICO) criteria with the population being older adults; intervention being AI; comparison being compared or not to other diagnostic methods; and outcome being FS with reported sensitivity, specificity, accuracy, or AUC values. The results were synthesized through information extraction and are presented in tables. Results: We identified 26 studies that met the inclusion criteria, 6 of which had a data set over 2000 and 3 with data sets below 100. Machine learning was the most widely used type of AI, employed in 18 studies. Moreover, of the 26 included studies, 9 used clinical data, with clinical histories being the most frequently used data type in this category. The remaining 17 studies used nonclinical data, most frequently involving activity monitoring using an inertial sensor in clinical and nonclinical contexts. Regarding the performance of each AI model, 10 studies achieved a value of precision, sensitivity, specificity, or AUC ?90. Conclusions: The findings of this scoping review clarify the overall status of recent studies using AI to identify and diagnose FS. Moreover, the findings show that the combined use of AI using clinical data along with nonclinical information such as the kinematics of inertial sensors that monitor activities in a nonclinical context could be an appropriate tool for the identification and diagnosis of FS. Nevertheless, some possible limitations of the evidence included in the review could be small sample sizes, heterogeneity of study designs, and lack of standardization in the AI models and diagnostic criteria used across studies. Future research is needed to validate AI systems with diverse data sources for diagnosing FS. AI should be used as a decision support tool for identifying FS, with data quality and privacy addressed, and the tool should be regularly monitored for performance after being integrated in clinical practice. UR - https://www.jmir.org/2023/1/e47346 UR - http://dx.doi.org/10.2196/47346 UR - http://www.ncbi.nlm.nih.gov/pubmed/37862082 ID - info:doi/10.2196/47346 ER - TY - JOUR AU - Zaidi, Maryum AU - Gazarian, Priscilla AU - Mattie, Heather AU - Sheldon, Kennedy Lisa AU - Gakumo, Ann C. PY - 2023/10/20 TI - Examining the Impact of Selected Sociodemographic Factors and Cancer-Related Fatalistic Beliefs on Patient Engagement via Health Information Technology Among Older Adults: Cross-Sectional Analysis JO - JMIR Aging SP - e44777 VL - 6 KW - health information technology KW - patient portals KW - older adults KW - digital health KW - self-management KW - mobile phone N2 - Background: Despite the role of health information technology (HIT) in patient engagement processes and government incentives for HIT development, research regarding HIT is lacking among older adults with a high burden of chronic diseases such as cancer. This study examines the role of selected sociodemographic factors and cancer-related fatalistic beliefs on patient engagement expressed through HIT use for patient engagement in adults aged ?65 years. We controlled for cancer diagnosis to account for its potential influence on patient engagement. Objective: This study has 2 aims: to investigate the role of sociodemographic factors such as race, education, poverty index, and psychosocial factors of cancer fatalistic beliefs in accessing and using HIT in older adults and to examine the association between access and use of HIT in the self-management domain of patient activation that serves as a precursor to patient engagement. Methods: This is a secondary data analysis of a subset of the Health Information National Trend Survey (Health Information National Trend Survey 4, cycle 3). The subset included individuals aged ?65 years with and without a cancer diagnosis. The relationships between access to and use of HIT to several sociodemographic variables and psychosocial factors of fatalistic beliefs were analyzed. Logistic and linear regression models were fit to study these associations. Results: This study included 180 individuals aged ?65 years with a cancer diagnosis and 398 without a diagnosis. This analysis indicated that having less than a college education level (P=<.001), being an individual from an ethnic and minority group (P=<.001), and living in poverty (P=.001) were significantly associated with decreased access to HIT. Reduced HIT use was associated with less than a college education (P=.001) and poverty(P=.02). This analysis also indicated that fatalistic beliefs about cancer were significantly associated with lower HIT use (P=.03). Specifically, a 1-point increase in the cancer fatalistic belief score was associated with a 36% decrease in HIT use. We found that controlling for cancer diagnosis did not affect the outcomes for sociodemographic variables or fatalistic beliefs about cancer. However, patients with access to HIT had a self-management domain of patient activation (SMD) score of 0.21 points higher (P=.003) compared with patients who did not have access. SMD score was higher by 0.28 points (P=.002) for individuals who used HIT and 0.14 points higher (P=.04) who had a prior diagnosis of cancer. Conclusions: Sociodemographic factors (education, race, poverty, and cancer fatalistic beliefs) impact HIT access and use in older adults, regardless of prior cancer diagnosis. Among older adults, HIT users report higher self-management, which is essential for patient activation and engagement. UR - https://aging.jmir.org/2023/1/e44777 UR - http://dx.doi.org/10.2196/44777 UR - http://www.ncbi.nlm.nih.gov/pubmed/37655786 ID - info:doi/10.2196/44777 ER - TY - JOUR AU - Husted, Skov Karina Louise AU - Brink-Kjær, Andreas AU - Fogelstrøm, Mathilde AU - Hulst, Pernille AU - Bleibach, Akita AU - Henneberg, Kaj-Åge AU - Sørensen, Dissing Helge Bjarup AU - Dela, Flemming AU - Jacobsen, Brings Jens Christian AU - Helge, Wulff Jørn PY - 2022/5/10 TI - A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study JO - JMIR Aging SP - e35696 VL - 5 IS - 2 KW - biological age KW - model development KW - principal component analysis KW - healthy aging KW - biomarkers KW - aging N2 - Background: Individual differences in the rate of aging and susceptibility to disease are not accounted for by chronological age alone. These individual differences are better explained by biological age, which may be estimated by biomarker prediction models. In the light of the aging demographics of the global population and the increase in lifestyle-related morbidities, it is interesting to invent a new biological age model to be used for health promotion. Objective: This study aims to develop a model that estimates biological age based on physiological biomarkers of healthy aging. Methods: Carefully selected physiological variables from a healthy study population of 100 women and men were used as biomarkers to establish an estimate of biological age. Principal component analysis was applied to the biomarkers and the first principal component was used to define the algorithm estimating biological age. Results: The first principal component accounted for 31% in women and 25% in men of the total variance in the biological age model combining mean arterial pressure, glycated hemoglobin, waist circumference, forced expiratory volume in 1 second, maximal oxygen consumption, adiponectin, high-density lipoprotein, total cholesterol, and soluble urokinase-type plasminogen activator receptor. The correlation between the corrected biological age and chronological age was r=0.86 (P<.001) and r=0.81 (P<.001) for women and men, respectively, and the agreement was high and unbiased. No difference was found between mean chronological age and mean biological age, and the slope of the regression line was near 1 for both sexes. Conclusions: Estimating biological age from these 9 biomarkers of aging can be used to assess general health compared with the healthy aging trajectory. This may be useful to evaluate health interventions and as an aid to enhance awareness of individual health risks and behavior when deviating from this trajectory. Trial Registration: ClinicalTrials.gov NCT03680768; https://clinicaltrials.gov/ct2/show/NCT03680768 International Registered Report Identifier (IRRID): RR2-10.2196/19209 UR - https://aging.jmir.org/2022/2/e35696 UR - http://dx.doi.org/10.2196/35696 UR - http://www.ncbi.nlm.nih.gov/pubmed/35536617 ID - info:doi/10.2196/35696 ER - TY - JOUR AU - Thapa, Rahul AU - Garikipati, Anurag AU - Shokouhi, Sepideh AU - Hurtado, Myrna AU - Barnes, Gina AU - Hoffman, Jana AU - Calvert, Jacob AU - Katzmann, Lynne AU - Mao, Qingqing AU - Das, Ritankar PY - 2022/4/1 TI - Predicting Falls in Long-term Care Facilities: Machine Learning Study JO - JMIR Aging SP - e35373 VL - 5 IS - 2 KW - vital signs KW - machine learning KW - blood pressure KW - skilled nursing facilities KW - independent living facilities KW - assisted living facilities KW - fall prediction KW - elderly care KW - elderly population KW - older adult KW - aging N2 - Background: Short-term fall prediction models that use electronic health records (EHRs) may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities. Objective: The aim of this study is to implement machine learning (ML) algorithms that use EHR data to predict a 3-month fall risk in residents from a variety of senior care facilities providing different levels of care. Methods: This retrospective study obtained EHR data (2007-2021) from Juniper Communities? proprietary database of 2785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performance of 3 ML-based fall prediction models and the Juniper Communities? fall risk assessment. Additional analyses were conducted to examine how changes in the input features, training data sets, and prediction windows affected the performance of these models. Results: The Extreme Gradient Boosting model exhibited the highest performance, with an area under the receiver operating characteristic curve of 0.846 (95% CI 0.794-0.894), specificity of 0.848, diagnostic odds ratio of 13.40, and sensitivity of 0.706, while achieving the best trade-off in balancing true positive and negative rates. The number of active medications was the most significant feature associated with fall risk, followed by a resident?s number of active diseases and several variables associated with vital signs, including diastolic blood pressure and changes in weight and respiratory rates. The combination of vital signs with traditional risk factors as input features achieved higher prediction accuracy than using either group of features alone. Conclusions: This study shows that the Extreme Gradient Boosting technique can use a large number of features from EHR data to make short-term fall predictions with a better performance than that of conventional fall risk assessments and other ML models. The integration of routinely collected EHR data, particularly vital signs, into fall prediction models may generate more accurate fall risk surveillance than models without vital signs. Our data support the use of ML models for dynamic, cost-effective, and automated fall predictions in different types of senior care facilities. UR - https://aging.jmir.org/2022/2/e35373 UR - http://dx.doi.org/10.2196/35373 UR - http://www.ncbi.nlm.nih.gov/pubmed/35363146 ID - info:doi/10.2196/35373 ER - TY - JOUR AU - Ferrario, Andrea AU - Luo, Minxia AU - Polsinelli, J. Angelina AU - Moseley, A. Suzanne AU - Mehl, R. Matthias AU - Yordanova, Kristina AU - Martin, Mike AU - Demiray, Burcu PY - 2022/3/8 TI - Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach JO - JMIR Aging SP - e28333 VL - 5 IS - 1 KW - cognitive aging KW - language complexity KW - social context KW - machine learning KW - natural language processing KW - Electronically Activated Recorder (EAR) KW - behavioral indicators N2 - Background: Language use and social interactions have demonstrated a close relationship with cognitive measures. It is important to improve the understanding of language use and behavioral indicators from social context to study the early prediction of cognitive decline among healthy populations of older adults. Objective: This study aimed at predicting an important cognitive ability, working memory, of 98 healthy older adults participating in a 4-day-long naturalistic observation study. We used linguistic measures, part-of-speech (POS) tags, and social context information extracted from 7450 real-life audio recordings of their everyday conversations. Methods: The methods in this study comprise (1) the generation of linguistic measures, representing idea density, vocabulary richness, and grammatical complexity, as well as POS tags with natural language processing (NLP) from the transcripts of real-life conversations and (2) the training of machine learning models to predict working memory using linguistic measures, POS tags, and social context information. We measured working memory using (1) the Keep Track test, (2) the Consonant Updating test, and (3) a composite score based on the Keep Track and Consonant Updating tests. We trained machine learning models using random forest, extreme gradient boosting, and light gradient boosting machine algorithms, implementing repeated cross-validation with different numbers of folds and repeats and recursive feature elimination to avoid overfitting. Results: For all three prediction routines, models comprising linguistic measures, POS tags, and social context information improved the baseline performance on the validation folds. The best model for the Keep Track prediction routine comprised linguistic measures, POS tags, and social context variables. The best models for prediction of the Consonant Updating score and the composite working memory score comprised POS tags only. Conclusions: The results suggest that machine learning and NLP may support the prediction of working memory using, in particular, linguistic measures and social context information extracted from the everyday conversations of healthy older adults. Our findings may support the design of an early warning system to be used in longitudinal studies that collects cognitive ability scores and records real-life conversations unobtrusively. This system may support the timely detection of early cognitive decline. In particular, the use of a privacy-sensitive passive monitoring technology would allow for the design of a program of interventions to enable strategies and treatments to decrease or avoid early cognitive decline. UR - https://aging.jmir.org/2022/1/e28333 UR - http://dx.doi.org/10.2196/28333 UR - http://www.ncbi.nlm.nih.gov/pubmed/35258457 ID - info:doi/10.2196/28333 ER - TY - JOUR AU - Rahman, Wasifur AU - Lee, Sangwu AU - Islam, Saiful Md AU - Antony, Nikhil Victor AU - Ratnu, Harshil AU - Ali, Rafayet Mohammad AU - Mamun, Al Abdullah AU - Wagner, Ellen AU - Jensen-Roberts, Stella AU - Waddell, Emma AU - Myers, Taylor AU - Pawlik, Meghan AU - Soto, Julia AU - Coffey, Madeleine AU - Sarkar, Aayush AU - Schneider, Ruth AU - Tarolli, Christopher AU - Lizarraga, Karlo AU - Adams, Jamie AU - Little, A. Max AU - Dorsey, Ray E. AU - Hoque, Ehsan PY - 2021/10/19 TI - Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study JO - J Med Internet Res SP - e26305 VL - 23 IS - 10 KW - Parkinson?s disease KW - speech analysis KW - improving access and equity in health care KW - mobile phone N2 - Background: Access to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases?fueled mostly by environmental pollution and an aging population?can be seen worldwide. The current projection of more than 12 million patients with PD worldwide by 2040 is only part of the picture given that more than 20% of patients with PD remain undiagnosed. Timely diagnosis and frequent assessment are key to ensure timely and appropriate medical intervention, thus improving the quality of life of patients with PD. Objective: In this paper, we propose a web-based framework that can help anyone anywhere around the world record a short speech task and analyze the recorded data to screen for PD. Methods: We collected data from 726 unique participants (PD: 262/726, 36.1% were women; non-PD: 464/726, 63.9% were women; average age 61 years) from all over the United States and beyond. A small portion of the data (approximately 54/726, 7.4%) was collected in a laboratory setting to compare the performance of the models trained with noisy home environment data against high-quality laboratory-environment data. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet, ?the quick brown fox jumps over the lazy dog.? We extracted both standard acoustic features (mel-frequency cepstral coefficients and jitter and shimmer variants) and deep learning?based embedding features from the speech data. Using these features, we trained several machine learning algorithms. We also applied model interpretation techniques such as Shapley additive explanations to ascertain the importance of each feature in determining the model?s output. Results: We achieved an area under the curve of 0.753 for determining the presence of self-reported PD by modeling the standard acoustic features through the XGBoost?a gradient-boosted decision tree model. Further analysis revealed that the widely used mel-frequency cepstral coefficient features and a subset of previously validated dysphonia features designed for detecting PD from a verbal phonation task (pronouncing ?ahh?) influence the model?s decision the most. Conclusions: Our model performed equally well on data collected in a controlled laboratory environment and in the wild across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with an audio-enabled device and help the participants screen for PD remotely, contributing to equity and access in neurological care. UR - https://www.jmir.org/2021/10/e26305 UR - http://dx.doi.org/10.2196/26305 UR - http://www.ncbi.nlm.nih.gov/pubmed/34665148 ID - info:doi/10.2196/26305 ER - TY - JOUR AU - Rivera-Rodriguez, Claudia AU - Cheung, Gary AU - Cullum, Sarah PY - 2021/1/6 TI - Using Big Data to Estimate Dementia Prevalence in New Zealand: Protocol for an Observational Study JO - JMIR Res Protoc SP - e20225 VL - 10 IS - 1 KW - routinely collected data KW - repeated measures KW - dementia KW - Alzheimer disease KW - modeling KW - complex sampling ? N2 - Background: Dementia describes a cluster of symptoms that includes memory loss; difficulties with thinking, problem solving, or language; and functional impairment. Dementia can be caused by a number of neurodegenerative diseases, such as Alzheimer disease and cerebrovascular disease. Currently in New Zealand, most of the systematically collected and detailed information on dementia is obtained through a suite of International Residential Assessment Instrument (interRAI) assessments, including the home care, contact assessment, and long-term care facility versions. These versions of interRAI are standardized comprehensive geriatric assessments. Patients are referred to have an interRAI assessment by the Needs Assessment and Service Coordination (NASC) services after a series of screening processes. Previous estimates of the prevalence and costs of dementia in New Zealand have been based on international studies with different populations and health and social care systems. This new local knowledge will have implications for estimating the demographic distribution and socioeconomic impact of dementia in New Zealand. Objective: This study investigates the prevalence of dementia, risk factors for dementia, and drivers of the informal cost of dementia among people registered in the NASC database in New Zealand. Methods: This study aims to analyze secondary data routinely collected by the NASC and interRAI (home care and contact assessment versions) databases between July 1, 2014, and July 1, 2019, in New Zealand. The databases will be linked to produce an integrated data set, which will be used to (1) investigate the sociodemographic and clinical risk factors associated with dementia and other neurological conditions, (2) estimate the prevalence of dementia using weighting methods for complex samples, and (3) identify the cost of informal care per client (in number of hours of care provided by unpaid carers) and the drivers of such costs. We will use design-based survey methods for the estimation of prevalence and generalized estimating equations for regression models and correlated and longitudinal data. Results: The results will provide much needed statistics regarding dementia prevalence and risk factors and the cost of informal care for people living with dementia in New Zealand. Potential health inequities for different ethnic groups will be highlighted, which can then be used by decision makers to inform the development of policy and practice. Conclusions: As of November 2020, there were no dementia prevalence studies or studies on informal care costs of dementia using national data from New Zealand. All existing studies have used data from other populations with substantially different demographic distributions. This study will give insight into the actual prevalence, risk factors, and informal care costs of dementia for the population with support needs in New Zealand. It will provide valuable information to improve health outcomes and better inform policy and planning. International Registered Report Identifier (IRRID): DERR1-10.2196/20225 UR - https://www.researchprotocols.org/2021/1/e20225 UR - http://dx.doi.org/10.2196/20225 UR - http://www.ncbi.nlm.nih.gov/pubmed/33404510 ID - info:doi/10.2196/20225 ER - TY - JOUR AU - Wilmink, Gerald AU - Dupey, Katherine AU - Alkire, Schon AU - Grote, Jeffrey AU - Zobel, Gregory AU - Fillit, M. Howard AU - Movva, Satish PY - 2020/9/10 TI - Artificial Intelligence?Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study JO - JMIR Aging SP - e19554 VL - 3 IS - 2 KW - health technology KW - artificial intelligence KW - AI KW - preventive KW - senior technology KW - assisted living KW - long-term services KW - long-term care providers N2 - Background: Wearables and artificial intelligence (AI)?powered digital health platforms that utilize machine learning algorithms can autonomously measure a senior?s change in activity and behavior and may be useful tools for proactive interventions that target modifiable risk factors. Objective: The goal of this study was to analyze how a wearable device and AI-powered digital health platform could provide improved health outcomes for older adults in assisted living communities. Methods: Data from 490 residents from six assisted living communities were analyzed retrospectively over 24 months. The intervention group (+CP) consisted of 3 communities that utilized CarePredict (n=256), and the control group (?CP) consisted of 3 communities (n=234) that did not utilize CarePredict. The following outcomes were measured and compared to baseline: hospitalization rate, fall rate, length of stay (LOS), and staff response time. Results: The residents of the +CP and ?CP communities exhibit no statistical difference in age (P=.64), sex (P=.63), and staff service hours per resident (P=.94). The data show that the +CP communities exhibited a 39% lower hospitalization rate (P=.02), a 69% lower fall rate (P=.01), and a 67% greater length of stay (P=.03) than the ?CP communities. The staff alert acknowledgment and reach resident times also improved in the +CP communities by 37% (P=.02) and 40% (P=.02), respectively. Conclusions: The AI-powered digital health platform provides the community staff with actionable information regarding each resident?s activities and behavior, which can be used to identify older adults that are at an increased risk for a health decline. Staff can use this data to intervene much earlier, protecting seniors from conditions that left untreated could result in hospitalization. In summary, the use of wearables and AI-powered digital health platform can contribute to improved health outcomes for seniors in assisted living communities. The accuracy of the system will be further validated in a larger trial. UR - http://aging.jmir.org/2020/2/e19554/ UR - http://dx.doi.org/10.2196/19554 UR - http://www.ncbi.nlm.nih.gov/pubmed/32723711 ID - info:doi/10.2196/19554 ER - TY - JOUR AU - Bayat, Sayeh AU - Naglie, Gary AU - Rapoport, J. Mark AU - Stasiulis, Elaine AU - Chikhaoui, Belkacem AU - Mihailidis, Alex PY - 2020/7/28 TI - Inferring Destinations and Activity Types of Older Adults From GPS Data: Algorithm Development and Validation JO - JMIR Aging SP - e18008 VL - 3 IS - 2 KW - outdoor mobility KW - older adults KW - GPS KW - life space KW - activity types KW - machine learning N2 - Background: Outdoor mobility is an important aspect of older adults? functional status. GPS has been used to create indicators reflecting the spatiotemporal dimensions of outdoor mobility for applications in health and aging. However, outdoor mobility is a multidimensional construct. There is, as of yet, no classification algorithm that groups and characterizes older adults? outdoor mobility based on its semantic aspects (ie, mobility intentions and motivations) by integrating geographic and domain knowledge. Objective: This study assesses the feasibility of using GPS to determine semantic dimensions of older adults? outdoor mobility, including destinations and activity types. Methods: A total of 5 healthy individuals, aged 65 years or older, carried a GPS device when traveling outside their homes for 4 weeks. The participants were also given a travel diary to record details of all excursions from their homes, including date, time, and destination information. We first designed and implemented an algorithm to extract destinations and infer activity types (eg, food, shopping, and sport) from the GPS data. We then evaluated the performance of the GPS-derived destination and activity information against the traditional diary method. Results: Our results detected the stop locations of older adults from their GPS data with an F1 score of 87%. On average, the extracted home locations were within a 40.18-meter (SD 1.18) distance of the actual home locations. For the activity-inference algorithm, our results reached an F1 score of 86% for all participants, suggesting a reasonable accuracy against the travel diary recordings. Our results also suggest that the activity inference?s accuracy measure differed by neighborhood characteristics (ie, Walk Score). Conclusions: We conclude that GPS technology is accurate for determining semantic dimensions of outdoor mobility. However, further improvements may be needed to develop a robust application of this system that can be adopted in clinical practice. UR - http://aging.jmir.org/2020/2/e18008/ UR - http://dx.doi.org/10.2196/18008 UR - http://www.ncbi.nlm.nih.gov/pubmed/32720647 ID - info:doi/10.2196/18008 ER - TY - JOUR AU - Peng, Li-Ning AU - Hsiao, Fei-Yuan AU - Lee, Wei-Ju AU - Huang, Shih-Tsung AU - Chen, Liang-Kung PY - 2020/6/11 TI - Comparisons Between Hypothesis- and Data-Driven Approaches for Multimorbidity Frailty Index: A Machine Learning Approach JO - J Med Internet Res SP - e16213 VL - 22 IS - 6 KW - multimorbidity frailty index KW - machine learning KW - random forest KW - unplanned hospitalizations KW - intensive care unit admissions KW - mortality N2 - Background: Using big data and the theory of cumulative deficits to develop the multimorbidity frailty index (mFI) has become a widely accepted approach in public health and health care services. However, constructing the mFI using the most critical determinants and stratifying different risk groups with dose-response relationships remain major challenges in clinical practice. Objective: This study aimed to develop the mFI by using machine learning methods that select variables based on the optimal fitness of the model. In addition, we aimed to further establish 4 entities of risk using a machine learning approach that would achieve the best distinction between groups and demonstrate the dose-response relationship. Methods: In this study, we used Taiwan?s National Health Insurance Research Database to develop a machine learning multimorbidity frailty index (ML-mFI) using the theory of cumulative diseases/deficits of an individual older person. Compared to the conventional mFI, in which the selection of diseases/deficits is based on expert opinion, we adopted the random forest method to select the most influential diseases/deficits that predict adverse outcomes for older people. To ensure that the survival curves showed a dose-response relationship with overlap during the follow-up, we developed the distance index and coverage index, which can be used at any time point to classify the ML-mFI of all subjects into the categories of fit, mild frailty, moderate frailty, and severe frailty. Survival analysis was conducted to evaluate the ability of the ML-mFI to predict adverse outcomes, such as unplanned hospitalizations, intensive care unit (ICU) admissions, and mortality. Results: The final ML-mFI model contained 38 diseases/deficits. Compared with conventional mFI, both indices had similar distribution patterns by age and sex; however, among people aged 65 to 69 years, the mean mFI and ML-mFI were 0.037 (SD 0.048) and 0.0070 (SD 0.0254), respectively. The difference may result from discrepancies in the diseases/deficits selected in the mFI and the ML-mFI. A total of 86,133 subjects aged 65 to 100 years were included in this study and were categorized into 4 groups according to the ML-mFI. Both the Kaplan-Meier survival curves and Cox models showed that the ML-mFI significantly predicted all outcomes of interest, including all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions at 1, 5, and 8 years of follow-up (P<.01). In particular, a dose-response relationship was revealed between the 4 ML-mFI groups and adverse outcomes. Conclusions: The ML-mFI consists of 38 diseases/deficits that can successfully stratify risk groups associated with all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions in older people, which indicates that precise, patient-centered medical care can be a reality in an aging society. UR - http://www.jmir.org/2020/6/e16213/ UR - http://dx.doi.org/10.2196/16213 UR - http://www.ncbi.nlm.nih.gov/pubmed/32525481 ID - info:doi/10.2196/16213 ER - TY - JOUR AU - Tarekegn, Adane AU - Ricceri, Fulvio AU - Costa, Giuseppe AU - Ferracin, Elisa AU - Giacobini, Mario PY - 2020/6/4 TI - Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches JO - JMIR Med Inform SP - e16678 VL - 8 IS - 6 KW - predictive modeling KW - frailty KW - machine learning KW - genetic programming KW - imbalanced dataset KW - elderly people KW - classification N2 - Background: Frailty is one of the most critical age-related conditions in older adults. It is often recognized as a syndrome of physiological decline in late life, characterized by a marked vulnerability to adverse health outcomes. A clear operational definition of frailty, however, has not been agreed so far. There is a wide range of studies on the detection of frailty and their association with mortality. Several of these studies have focused on the possible risk factors associated with frailty in the elderly population while predicting who will be at increased risk of frailty is still overlooked in clinical settings. Objective: The objective of our study was to develop predictive models for frailty conditions in older people using different machine learning methods based on a database of clinical characteristics and socioeconomic factors. Methods: An administrative health database containing 1,095,612 elderly people aged 65 or older with 58 input variables and 6 output variables was used. We first identify and define six problems/outputs as surrogates of frailty. We then resolve the imbalanced nature of the data through resampling process and a comparative study between the different machine learning (ML) algorithms ? Artificial neural network (ANN), Genetic programming (GP), Support vector machines (SVM), Random Forest (RF), Logistic regression (LR) and Decision tree (DT) ? was carried out. The performance of each model was evaluated using a separate unseen dataset. Results: Predicting mortality outcome has shown higher performance with ANN (TPR 0.81, TNR 0.76, accuracy 0.78, F1-score 0.79) and SVM (TPR 0.77, TNR 0.80, accuracy 0.79, F1-score 0.78) than predicting the other outcomes. On average, over the six problems, the DT classifier has shown the lowest accuracy, while other models (GP, LR, RF, ANN, and SVM) performed better. All models have shown lower accuracy in predicting an event of an emergency admission with red code than predicting fracture and disability. In predicting urgent hospitalization, only SVM achieved better performance (TPR 0.75, TNR 0.77, accuracy 0.73, F1-score 0.76) with the 10-fold cross validation compared with other models in all evaluation metrics. Conclusions: We developed machine learning models for predicting frailty conditions (mortality, urgent hospitalization, disability, fracture, and emergency admission). The results show that the prediction performance of machine learning models significantly varies from problem to problem in terms of different evaluation metrics. Through further improvement, the model that performs better can be used as a base for developing decision-support tools to improve early identification and prediction of frail older adults. UR - http://medinform.jmir.org/2020/6/e16678/ UR - http://dx.doi.org/10.2196/16678 UR - http://www.ncbi.nlm.nih.gov/pubmed/32442149 ID - info:doi/10.2196/16678 ER - TY - JOUR AU - Itoh, Sakiko AU - Hikichi, Hiroyuki AU - Murayama, Hiroshi AU - Ishimaru, Miho AU - Ogata, Yasuko AU - Yasunaga, Hideo PY - 2018/07/25 TI - Association Between Advanced Care Management and Progression of Care Needs Level in Long-Term Care Recipients: Retrospective Cohort Study JO - JMIR Aging SP - e11117 VL - 1 IS - 2 KW - community health services, health services for the aged, integrated care, long-term care, patient care planning N2 - Background: Long-term care insurance systems in Japan started a special senior care program overseen by qualified care managers (also known as advanced care managers). However, the relationship between advanced care management and outcomes in long-term care recipients remains unknown. Objective: We aimed to compare the outcome of long-term care recipients using facilities with advanced care management and conventional care management, in terms of care needs level progression. Methods: We conducted a retrospective cohort study using the Survey of Long-Term Care Benefit Expenditures in Japan. We identified those aged ?65 years who were newly designated a care need level of 3, and received long-term care services between April 2009 and March 2014 in Tokyo. We compared survival without progression of care needs level between the groups, with and without advanced care management, using the Kaplan-Meier method. Factors affecting the outcomes were determined using a multivariable logistic regression model fitted with a generalized estimating equation. Results: Of 45,330 eligible persons, 12,903 (28.46%) received long-term care based on advanced care management. The average duration of progression-free survival was 17.4 (SD 10.2) months. The proportions of five-year cumulative progression-free survival were 41.2% and 32.8% in those with and without advanced care management, respectively. The group with advanced care management had significantly lower care needs levels (odds ratio 0.77, 95% CI, 0.72-0.82, P<.001). Conclusions: Advanced care management was significantly associated with improved care needs levels. UR - http://aging.jmir.org/2018/2/e11117/ UR - http://dx.doi.org/10.2196/11117 UR - http://www.ncbi.nlm.nih.gov/pubmed/31518239 ID - info:doi/10.2196/11117 ER - TY - JOUR AU - Guo, Yanting AU - Zheng, Gang AU - Fu, Tianyun AU - Hao, Shiying AU - Ye, Chengyin AU - Zheng, Le AU - Liu, Modi AU - Xia, Minjie AU - Jin, Bo AU - Zhu, Chunqing AU - Wang, Oliver AU - Wu, Qian AU - Culver, S. Devore AU - Alfreds, T. Shaun AU - Stearns, Frank AU - Kanov, Laura AU - Bhatia, Ajay AU - Sylvester, G. Karl AU - Widen, Eric AU - McElhinney, B. Doff AU - Ling, Bruce Xuefeng PY - 2018/06/04 TI - Assessing Statewide All-Cause Future One-Year Mortality: Prospective Study With Implications for Quality of Life, Resource Utilization, and Medical Futility JO - J Med Internet Res SP - e10311 VL - 20 IS - 6 KW - One-year mortality risk prediction KW - electronic medical records KW - quality of life KW - healthcare resource utilization KW - social determinants N2 - Background: For many elderly patients, a disproportionate amount of health care resources and expenditures is spent during the last year of life, despite the discomfort and reduced quality of life associated with many aggressive medical approaches. However, few prognostic tools have focused on predicting all-cause 1-year mortality among elderly patients at a statewide level, an issue that has implications for improving quality of life while distributing scarce resources fairly. Objective: Using data from a statewide elderly population (aged ?65 years), we sought to prospectively validate an algorithm to identify patients at risk for dying in the next year for the purpose of minimizing decision uncertainty, improving quality of life, and reducing futile treatment. Methods: Analysis was performed using electronic medical records from the Health Information Exchange in the state of Maine, which covered records of nearly 95% of the statewide population. The model was developed from 125,896 patients aged at least 65 years who were discharged from any care facility in the Health Information Exchange network from September 5, 2013, to September 4, 2015. Validation was conducted using 153,199 patients with same inclusion and exclusion criteria from September 5, 2014, to September 4, 2016. Patients were stratified into risk groups. The association between all-cause 1-year mortality and risk factors was screened by chi-squared test and manually reviewed by 2 clinicians. We calculated risk scores for individual patients using a gradient tree-based boost algorithm, which measured the probability of mortality within the next year based on the preceding 1-year clinical profile. Results: The development sample included 125,896 patients (72,572 women, 57.64%; mean 74.2 [SD 7.7] years). The final validation cohort included 153,199 patients (88,177 women, 57.56%; mean 74.3 [SD 7.8] years). The c-statistic for discrimination was 0.96 (95% CI 0.93-0.98) in the development group and 0.91 (95% CI 0.90-0.94) in the validation cohort. The mortality was 0.99% in the low-risk group, 16.75% in the intermediate-risk group, and 72.12% in the high-risk group. A total of 99 independent risk factors (n=99) for mortality were identified (reported as odds ratios; 95% CI). Age was on the top of list (1.41; 1.06-1.48); congestive heart failure (20.90; 15.41-28.08) and different tumor sites were also recognized as driving risk factors, such as cancer of the ovaries (14.42; 2.24-53.04), colon (14.07; 10.08-19.08), and stomach (13.64; 3.26-86.57). Disparities were also found in patients? social determinants like respiratory hazard index (1.24; 0.92-1.40) and unemployment rate (1.18; 0.98-1.24). Among high-risk patients who expired in our dataset, cerebrovascular accident, amputation, and type 1 diabetes were the top 3 diseases in terms of average cost in the last year of life. Conclusions: Our study prospectively validated an accurate 1-year risk prediction model and stratification for the elderly population (?65 years) at risk of mortality with statewide electronic medical record datasets. It should be a valuable adjunct for helping patients to make better quality-of-life choices and alerting care givers to target high-risk elderly for appropriate care and discussions, thus cutting back on futile treatment. UR - http://www.jmir.org/2018/6/e10311/ UR - http://dx.doi.org/10.2196/10311 UR - http://www.ncbi.nlm.nih.gov/pubmed/29866643 ID - info:doi/10.2196/10311 ER - TY - JOUR AU - He, Zhe AU - Bian, Jiang AU - Carretta, J. Henry AU - Lee, Jiwon AU - Hogan, R. William AU - Shenkman, Elizabeth AU - Charness, Neil PY - 2018/04/12 TI - Prevalence of Multiple Chronic Conditions Among Older Adults in Florida and the United States: Comparative Analysis of the OneFlorida Data Trust and National Inpatient Sample JO - J Med Internet Res SP - e137 VL - 20 IS - 4 KW - medical informatics KW - chronic disease KW - comorbidity KW - geriatrics N2 - Background: Older patients with multiple chronic conditions are often faced with increased health care needs and subsequent higher medical costs, posing significant financial burden to patients, their caregivers, and the health care system. The increasing adoption of electronic health record systems and the proliferation of clinical data offer new opportunities for prevalence studies and for population health assessment. The last few years have witnessed an increasing number of clinical research networks focused on building large collections of clinical data from electronic health records and claims to make it easier and less costly to conduct clinical research. Objective: The aim of this study was to compare the prevalence of common chronic conditions and multiple chronic conditions in older adults between Florida and the United States using data from the OneFlorida Clinical Research Consortium and the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS). Methods: We first analyzed the basic demographic characteristics of the older adults in 3 datasets?the 2013 OneFlorida data, the 2013 HCUP NIS data, and the combined 2012 to 2016 OneFlorida data. Then we analyzed the prevalence of each of the 25 chronic conditions in each of the 3 datasets. We stratified the analysis of older adults with hypertension, the most prevalent condition. Additionally, we examined trends (ie, overall trends and then by age, race, and gender) in the prevalence of discharge records representing multiple chronic conditions over time for the OneFlorida (2012-2016) and HCUP NIS cohorts (2003-2013). Results: The rankings of the top 10 prevalent conditions are the same across the OneFlorida and HCUP NIS datasets. The most prevalent multiple chronic conditions of 2 conditions among the 3 datasets were?hyperlipidemia and hypertension; hypertension and ischemic heart disease; diabetes and hypertension; chronic kidney disease and hypertension; anemia and hypertension; and hyperlipidemia and ischemic heart disease. We observed increasing trends in multiple chronic conditions in both data sources. Conclusions: The results showed that chronic conditions and multiple chronic conditions are prevalent in older adults across Florida and the United States. Even though slight differences were observed, the similar estimates of prevalence of chronic conditions and multiple chronic conditions across OneFlorida and HCUP NIS suggested that clinical research data networks such as OneFlorida, built from heterogeneous data sources, can provide rich data resources for conducting large-scale secondary data analyses. UR - http://www.jmir.org/2018/4/e137/ UR - http://dx.doi.org/10.2196/jmir.8961 UR - http://www.ncbi.nlm.nih.gov/pubmed/29650502 ID - info:doi/10.2196/jmir.8961 ER -