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Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis

Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis

The most common MACCEs components were 1-year mortality (n=3), followed by 30-day mortality (n=2) and in-hospital mortality (n=2). All studies included in this review reported that ML-based models outperformed CRS, including GRACE and TIMI, demonstrating higher accuracy and discriminatory power. We critically appraised the 10 studies included in the review (Multimedia Appendix 2).

Min-Young Yu, Hae Young Yoo, Ga In Han, Eun-Jung Kim, Youn-Jung Son

J Med Internet Res 2025;27:e76215

Natural Language Processing for Identification of Hospitalized People Who Use Drugs: Cohort Study

Natural Language Processing for Identification of Hospitalized People Who Use Drugs: Cohort Study

Barriers to optimization of health care for hospitalized PWUD include undertreatment of pain and substance use disorders, which have been linked to discharges before medical optimization and higher rates of readmission and mortality [7-9]. Best practices for managing PWUD in a hospitalized setting include addiction care itself as well as treatment and prevention of life-threatening infections [10].

Taisuke Sato, Emily D Grussing, Ruchi Patel, Jessica Ridgway, Joji Suzuki, Benjamin Sweigart, Robert Miller, Alysse G Wurcel

JMIR AI 2025;4:e63147

Association Between Comorbidity Clusters and Mortality in Patients With Cancer: Predictive Modeling Using Machine Learning Approaches of Data From the United States and Hong Kong

Association Between Comorbidity Clusters and Mortality in Patients With Cancer: Predictive Modeling Using Machine Learning Approaches of Data From the United States and Hong Kong

The individuals in the respiratory cluster had a significantly higher risk of mortality (P=.003) if they had a lower income-to-poverty ratio, while those in the metabolic cluster (P=.02) had a higher risk of mortality if they reported a higher income-to-poverty ratio.

Chun Sing Lam, Rong Hua, Herbert Ho-Fung Loong, Chun-Kit Ngan, Yin Ting Cheung

JMIR Cancer 2025;11:e71937

AI Predictive Model of Mortality and Intensive Care Unit Admission in the COVID-19 Pandemic: Retrospective Population Cohort Study of 12,000 Patients

AI Predictive Model of Mortality and Intensive Care Unit Admission in the COVID-19 Pandemic: Retrospective Population Cohort Study of 12,000 Patients

Feature selection using recursive feature elimination with k-fold cross-validation and AUC resulted in 60 selected features for both mortality and ICU admission prediction. Figure 2 illustrates how models’ AUCs remain stable, with values equal to or greater than 0.90, up to 60 features. Note that while the number of features is the same, the selected features differ depending on whether the prediction is for mortality or ICU admission.

Jose Manuel Ruiz Giardin, Óscar Garnica, Nieves Mesa Plaza, Juan Víctor SanMartín López, Ana Farfán Sedano, Elena Madroñal Cerezo, Miguel Ángel Duarte Millán, Aida Izquierdo Martínez, Luis Rivas, Marta Rivilla, Alejandro Morales Ortega, Begoña Frutos Pérez, Cristina De Ancos Aracil, Ruth Calderón, Guillermo Soria Fernandez, Jorge Marrero Francés, David Bernal Bello, Jose Ángel Satué Bartolomé, María Toledano Macías, Sara Piedrabuena García, Marta Guerrero Santillán, Rafael Cristóbal, Belen Mora, Laura Velázquez Ríos, Vanesa García de Viedma, Paula Cuenca Ruiz, Ibone Ayala Larrañaga, Lorena Carpintero, Celia Lara, Alvaro Ricardo Llerena, Virginia García Bermúdez, Gema Delgado Cárdenas, Paloma Pardo Rovira, Elena Tejero Sánchez, Maria Jesús Domínguez García, Carolina Mariño, Cristina Bravo, Ana Ontañon, Mario García, Jose Ignacio Hidalgo Pérez, Santiago Prieto Menchero, Natalia González Pereira, Sonia Gonzalo Pascua, Jorge Tarancón Rey, Luis Antonio Lechuga Suárez, FUENCOVID

J Med Internet Res 2025;27:e70674

Misrepresentation of Overall and By-Gender Mortality Causes in Film Using Online, Crowd-Sourced Data: Quantitative Analysis

Misrepresentation of Overall and By-Gender Mortality Causes in Film Using Online, Crowd-Sourced Data: Quantitative Analysis

The aim of this study is to understand the extent to which cinematic deaths in Cinemorgue reflect real-world mortality by gender and cause of death. There are 2 main data sources for this project: the Cinemorgue Wiki database of film deaths [24] and the 2021 NVSS Leading Causes of Mortality Report [1].

Calla Glavin Beauregard, Christopher M Danforth, Peter Sheridan Dodds

JMIR Form Res 2025;9:e70853

Hospitalization and Mortality in Brazilian Children and Adolescents Due to COVID-19: Retrospective Study

Hospitalization and Mortality in Brazilian Children and Adolescents Due to COVID-19: Retrospective Study

An American study reports that pediatric patients with a recent relapse of cancer have a higher chance of all-cause mortality when infected with COVID-19 [12]. The objectives of this study were to evaluate the mortality of children and adolescents and compare it with that of adults in a cohort of 8986 patients hospitalized for COVID-19 in a university hospital complex in Brazil.

Ana Carolina Pereira de Godoy, Reinaldo Bulgarelli Bestetti

JMIR Pediatr Parent 2025;8:e67546

Early Prediction of Mortality Risk in Acute Respiratory Distress Syndrome: Systematic Review and Meta-Analysis

Early Prediction of Mortality Risk in Acute Respiratory Distress Syndrome: Systematic Review and Meta-Analysis

These interventions are crucial for reducing mortality rates and improving the overall prognosis for affected patients. Consequently, the early identification of potential mortality risks in patients with ARDS, coupled with timely and effective interventions, holds the promise of reversing adverse clinical outcomes and improving survival rates [7]. Currently, no reliable scoring methods are available for predicting mortality in patients with ARDS within the realm of clinical practice [8].

Ruimin Tan, Chen Ge, Zhe Li, Yating Yan, He Guo, Wenjing Song, Qiong Zhu, Quansheng Du

J Med Internet Res 2025;27:e70537

Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption

Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption

Many countries worldwide have embraced mammographic screening programs as a vital tool for identifying breast cancer in its early stages, significantly reducing the risk of associated mortality [2]. Despite the perceived advantages, numerous challenges remain in the interpretation of screening mammograms. First, the high volume of screenings, combined with the requirement for independent, blinded double-reading by radiologists, places significant pressure on the existing radiology workforce [3].

Serene Goh, Rachel Sze Jen Goh, Bryan Chong, Qin Xiang Ng, Gerald Choon Huat Koh, Kee Yuan Ngiam, Mikael Hartman

J Med Internet Res 2025;27:e62941

Effects of the National Institutes of Health Stroke Scale and Modified Rankin Scale on Predictive Models of 30-Day Nonelective Readmission and Mortality After Ischemic Stroke: Cohort Study

Effects of the National Institutes of Health Stroke Scale and Modified Rankin Scale on Predictive Models of 30-Day Nonelective Readmission and Mortality After Ischemic Stroke: Cohort Study

For our outcomes, we examined readmission within 30 days or mortality within 30 days postdischarge. For readmission and mortality within 30 days, there were no missing data. For those who were readmitted to a non-Kaiser hospital, we identified their readmission through claim data. The study was limited to health plan members only, for whom we had full data on mortality.

Mai N Nguyen-Huynh, Janet Alexander, Zheng Zhu, Melissa Meighan, Gabriel Escobar

JMIR Med Inform 2025;13:e69102

Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis

Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis

Accurate and timely diagnosis is essential for the treatment of pediatric respiratory illnesses, which remain a leading cause of morbidity and mortality among children worldwide [1,2]. Auscultation of lung sounds is the most widely used method of respiratory diagnosis due to its simplicity, cost-effectiveness, and safety.

Ji Soo Park, Sa-Yoon Park, Jae Won Moon, Kwangsoo Kim, Dong In Suh

J Med Internet Res 2025;27:e66491