This paper is in the following e-collection/theme issue:

Machine Learning (1465) Clinical Information and Decision Making (1407) Big Data Analytics for Elder Care (22) Falls Prevention in the Elderly (69) Geroinformatics and Electronic Clinical Information/Decision Making in Geriatrics (20) Frailty Detection, Assessment and Prediction (28)

Published on in Vol 8, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16678, first published .
Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches

Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches

Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches

Authors of this article:

Adane Tarekegn1 Author Orcid Image ;   Fulvio Ricceri2, 3 Author Orcid Image ;   Giuseppe Costa2, 3 Author Orcid Image ;   Elisa Ferracin3 Author Orcid Image ;   Mario Giacobini4 Author Orcid Image
Article Authors Cited by (45) Tweetations (7) Metrics

Journals

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  21. Liu Q, Yang L, Shi Z, Yu J, Si H, Jin Y, Bian Y, Li Y, Ji L, Qiao X, Wang W, Liu H, Zhang M, Wang C. Development and validation of a preliminary clinical support system for measuring the probability of incident 2-year (pre)frailty among community-dwelling older adults: A prospective cohort study. International Journal of Medical Informatics 2023;177:105138 View
  22. Fan S, Ye J, Xu Q, Peng R, Hu B, Pei Z, Yang Z, Xu F. Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty. Frontiers in Public Health 2023;11 View
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Books/Policy Documents

  1. Nega Tarekegn A, Alaya Cheikh F, Sajjad M, Ullah M. Artificial Intelligence and Soft Computing. View
  2. Zeleke S, Bochicchio M. Ambient Assisted Living. View