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

Machine Learning (1190) Clinical Information and Decision Making (1115) Big Data Analytics for Elder Care (16) Falls Prevention in the Elderly (58) Geroinformatics and Electronic Clinical Information/Decision Making in Geriatrics (16) Frailty Detection, Assessment and Prediction (15)

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 Tarekegn 1 Author Orcid Image ;   Fulvio Ricceri 2, 3 Author Orcid Image ;   Giuseppe Costa 2, 3 Author Orcid Image ;   Elisa Ferracin 3 Author Orcid Image ;   Mario Giacobini 4 Author Orcid Image
Article Authors Cited by (31) Tweetations (7) Metrics

Journals

  1. Wojtusiak J, Asadzadehzanjani N, Levy C, Alemi F, Williams A. Computational Barthel Index: an automated tool for assessing and predicting activities of daily living among nursing home patients. BMC Medical Informatics and Decision Making 2021;21(1) View
  2. Tarekegn A, Giacobini M, Michalak K. A review of methods for imbalanced multi-label classification. Pattern Recognition 2021;118:107965 View
  3. Urbano D, Restivo M, Barbosa M, Fernandes Â, Abreu P, Chousal M, Coelho T. Handgrip Strength Time Profile and Frailty: An Exploratory Study. Applied Sciences 2021;11(11):5134 View
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  5. Panchal S, Naik A, Kokare M, Pachade S, Naigaonkar R, Phadnis P, Bhange A. Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0: A Dataset of Frequently and Rarely Identified Diseases. Data 2023;8(2):29 View
  6. Thandi M, Wong S, Aponte-Hao S, Grandy M, Mangin D, Singer A, Williamson T. Strategies for working across Canadian practice-based research and learning networks (PBRLNs) in primary care: focus on frailty. BMC Family Practice 2021;22(1) View
  7. Lin C, Chien T, Chen Y, Lee Y, Su S. An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel. Medicine 2022;101(4):e28697 View
  8. Suh B, Yu H, Kim H, Lee S, Kong S, Kim J, Choi J. Interpretable Deep-Learning Approaches for Osteoporosis Risk Screening and Individualized Feature Analysis Using Large Population-Based Data: Model Development and Performance Evaluation. Journal of Medical Internet Research 2023;25:e40179 View
  9. Silva G, Zaruz M, Milagre S, de Oliveira Andrade A, Pereira A. Assessment of the performance of classifiers in the discrimination of healthy adults and elderly individuals through functional fitness tasks. Research on Biomedical Engineering 2023;39(1):245 View
  10. Klunder J, Panneman S, Wallace E, de Vries R, Joling K, Maarsingh O, van Hout H, Yon D. Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review. PLOS ONE 2022;17(9):e0275116 View
  11. Sepúlveda M, Arauna D, García F, Albala C, Palomo I, Fuentes E. Frailty in Aging and the Search for the Optimal Biomarker: A Review. Biomedicines 2022;10(6):1426 View
  12. Wang J, Wang S, Zhu M, Yang T, Yin Q, Hou Y. Risk Prediction of Major Adverse Cardiovascular Events Occurrence Within 6 Months After Coronary Revascularization: Machine Learning Study. JMIR Medical Informatics 2022;10(4):e33395 View
  13. Heyl J, Hardy F, Tucker K, Hopper A, Marchã M, Navaratnam A, Briggs T, Yates J, Day J, Wheeler A, Eve-Jones S, Gray W. Frailty, Comorbidity, and Associations With In-Hospital Mortality in Older COVID-19 Patients: Exploratory Study of Administrative Data. Interactive Journal of Medical Research 2022;11(2):e41520 View
  14. Oliosi E, Guede-Fernández F, Londral A. Machine Learning Approaches for the Frailty Screening: A Narrative Review. International Journal of Environmental Research and Public Health 2022;19(14):8825 View
  15. Martin J, Crane-Droesch A, Lapite F, Puhl J, Kmiec T, Silvestri J, Ungar L, Kinosian B, Himes B, Hubbard R, Diamond J, Ahya V, Sims M, Halpern S, Weissman G. Development and validation of a prediction model for actionable aspects of frailty in the text of clinicians’ encounter notes. Journal of the American Medical Informatics Association 2021;29(1):109 View
  16. Wu Y, Jia M, Xiang C, Fang Y. Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective. BMC Geriatrics 2022;22(1) View
  17. Hu T, Chow J, Chien T, Chou W. Detecting dengue fever in children using online Rasch analysis to develop algorithms for parents: An APP development and usability study. Medicine 2023;102(13):e33296 View
  18. Pavon J, Previll L, Woo M, Henao R, Solomon M, Rogers U, Olson A, Fischer J, Leo C, Fillenbaum G, Hoenig H, Casarett D. Machine learning functional impairment classification with electronic health record data. Journal of the American Geriatrics Society 2023;71(9):2822 View
  19. Zhang L, Zeng X, He F, Huang X. Inflammatory biomarkers of frailty: A review. Experimental Gerontology 2023;179:112253 View
  20. Leghissa M, Carrera Á, Iglesias C. Machine learning approaches for frailty detection, prediction and classification in elderly people: A systematic review. International Journal of Medical Informatics 2023;178:105172 View
  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
  23. Velazquez-Diaz D, Arco J, Ortiz A, Pérez-Cabezas V, Lucena-Anton D, Moral-Munoz J, Galán-Mercant A. Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review. Journal of Medical Internet Research 2023;25:e47346 View
  24. Du Y, Huang W. Portfolio Allocation with Medical Expenditure Risk-A Life Cycle Model and Machine Learning Analysis. Journal of Regional Economics 2023;2(1) View
  25. Bai A, Zhao M, Zhang T, Yang C, Yan J, Wang G, Zhang P, Xu W, Hu Y. Development and validation of a nomogram-assisted tool to predict potentially reversible cognitive frailty in Chinese community-living older adults. Aging Clinical and Experimental Research 2023;35(10):2145 View
  26. Bottrighi A, Pennisi M. Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community. Information 2023;14(9):513 View
  27. Vafaei A, Wu Y, Curcio C, Gomes C, Auais M, Gomez F. A Regression Tree Analysis to Identify Factors Predicting Frailty: The International Mobility in Aging Study. Gerontology 2023;69(2):130 View
  28. Tarekegn A, Sajjad M, Cheikh F, Ullah M, Muhammad K. Efficient Human Gait Activity Recognition Based on Sensor Fusion and Intelligent Stacking Framework. IEEE Sensors Journal 2023;23(22):28355 View

Books/Policy Documents

  1. Nega Tarekegn A, Alaya Cheikh F, Sajjad M, Ullah M. Artificial Intelligence and Soft Computing. View