JMIR Aging - Search

View query in Help articles search

Field

e.g. mhealth

Operator

Field

Criteria

e.g. John Smith or 0000-0001-6479-5330

Exact Match for Criteria

Yes No

Search Results (1 to 10 of 6372 Results)

Download search results: CSV END BibTex RIS

Sort by:

Children’s Improvement After Language and Rhythm Training With the Digital Medical Device Poppins for Dyslexia: Single-Arm Intervention Study

Screenshots of rhythm tasks in the Poppins medical device: (A) In the Temple of Music, the child must recreate the rhythm of a melody using predefined rhythmic blocks; (B) in the King Song, the child must sing the displayed syllables in rhythm; (C) in the Beat Jumper, the child must make their character jump in rhythm by shaking the tablet; and (D) in Pop'n Run, the child must keep the rhythm by tapping the screen with their finger.

Charline Grossard, Melanie Descamps, Hugues Pellerin, François Vonthron, David Cohen

JMIR Serious Games 2025;13:e76435

Download Citation: END BibTex RIS

Download PDF Download XML

Consumer Data is Key to Artificial Intelligence Value: Welcome to the Health Care Future

FHIR builds upon its predecessor, the HL7 Consolidated Clinical Document Architecture (C-CDA) [25], a document-based standard used to capture a “point-in-time” snapshot of a consumer health record. Unlike C-CDA, FHIR uses modern web technology, such as RESTful APIs, JSON, and XML to enable consistent data exchange. Importantly, FHIR APIs allow for “real-time” data exchange through their discrete resource design.

James C

J Particip Med 2025;17:e68261

Download Citation: END BibTex RIS

Download PDF Download XML

Role and Use of Race in Artificial Intelligence and Machine Learning Models Related to Health

When it pertains to the use of race in AI and ML models for health, the purpose of a model could be twofold, namely: (1) a model that answers a non–race related question (eg, develop a 1-year mortality risk estimation model for all patients) but whose performance may differ across racial groups, or (b) a model that specifically evaluates a question or difference based on race (eg, examine how cancer risk factors and outcomes differ by race).

Martin C Were, Ang Li, Bradley A Malin, Zhijun Yin, Joseph R Coco, Benjamin X Collins, Ellen Wright Clayton, Laurie L Novak, Rachele Hendricks-Sturrup, Abiodun O Oluyomi, Shilo Anders, Chao Yan

J Med Internet Res 2025;27:e73996

Download Citation: END BibTex RIS

Download PDF Download XML

Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach

C values were ranging from 0.01, 0.1, 1, and 10, with gamma values of 0.01, 0.1, and 1, coef0 values of 0 and 1, and lastly class weights of none and balanced. Evaluation of the model was obtained and inspected using the classification_report function, by focusing on harmonization between F1-score, recall, accuracy, precision, and macro-avg values, we evaluated the models’ performance on training and test sets to ensure reliability of the model with no over- or underfitting present.

David Agustriawan, Adithama Mulia, Marlinda Vasty Overbeek, Vincent Kurniawan, Jheno Syechlo, Moeljono Widjaja, Muhammad Imran Ahmad

JMIR Bioinform Biotech 2025;6:e72423

Download Citation: END BibTex RIS

Download PDF Download XML

Optimizing Feature Selection and Machine Learning Algorithms for Early Detection of Prediabetes Risk: Comparative Study

ALB: albumin; ALT: alanine aminotransferase; AST: aspartate transaminase; BUN: blood urea nitrogen; DB: direct bilirubin; GLB: globulin; HDL-C: high-density lipoprotein cholesterol; IB: indirect bilirubin; LDL-C: low-density lipoprotein cholesterol; SBP: systolic blood pressure; SCr: serum creatinine; T-BIL: total bilirubin; TC: total cholesterol; TG: triglyceride; TP: total protein; UA: uric acid.

Mahmoud B Almadhoun, MA Burhanuddin

JMIR Bioinform Biotech 2025;6:e70621

Download Citation: END BibTex RIS

Download PDF Download XML