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

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
  4. Akbari G, Nikkhoo M, Wang L, Chen C, Han D, Lin Y, Chen H, Cheng C. Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach. Sensors 2021;21(12):4017 View
  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
  29. Bohn L, Drouin S, McFall G, Rolfson D, Andrew M, Dixon R. Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer’s disease spectrum: a COMPASS-ND study. BMC Geriatrics 2023;23(1) View
  30. Pan C, Luo H, Cheung G, Zhou H, Cheng R, Cullum S, Wu C. Identifying Frailty in Older Adults Receiving Home Care Assessment Using Machine Learning: Longitudinal Observational Study on the Role of Classifier, Feature Selection, and Sample Size. JMIR AI 2024;3:e44185 View
  31. Koh V, Xuan L, Zhe T, Singh N, B. Matchar D, Chan A. Performance of digital technologies in assessing fall risks among older adults with cognitive impairment: a systematic review. GeroScience 2024;46(3):2951 View
  32. de Maio Nascimento M, Ihle A, Gouveia É, Marques A. Dynamic associations between frailty and cognition over 4 years: A population-based study on adults aged ≥50 from 12 European countries. Journal of Affective Disorders 2024;354:536 View
  33. Liu Q, Si H, Li Y, Zhou W, Yu J, Bian Y, Wang C. Development and validation of a risk scoring tool for predicting incident reversible cognitive frailty among community‐dwelling older adults: A prospective cohort study. Geriatrics & Gerontology International 2024;24(9):874 View
  34. Rao V, Valdez D, Muralidharan R, Agley J, Eddens K, Dendukuri A, Panth V, Parker M. Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis. Journal of Medical Internet Research 2024;26:e57885 View
  35. Tarekegn A, Michalak K, Costa G, Ricceri F, Giacobini M. Predicting Multiple Outcomes Associated with Frailty based on Imbalanced Multi-label Classification. Journal of Healthcare Informatics Research 2024;8(4):594 View
  36. Bai C, Mardini M. Advances of artificial intelligence in predicting frailty using real-world data: A scoping review. Ageing Research Reviews 2024;101:102529 View
  37. Exmann C, Kooijmans E, Joling K, Burchell G, Hoogendijk E, van Hout H. Mortality prediction models for community-dwelling older adults: A systematic review. Ageing Research Reviews 2024;101:102525 View
  38. Papa R, Balducci F, Franceschini G, Pompili M, De Marco M, Roca J, González-Colom R, Monterde D. Applicability of the adjusted morbidity groups algorithm for healthcare programming: results of a pilot study in Italy. BMC Public Health 2024;24(1) View
  39. Ren W, Zheng Y, Zhang W, Qing D, Zeng X, Li G. A novel ensemble over-sampling approach based Chebyshev inequality for imbalanced multi-label data. Neurocomputing 2025;612:128717 View
  40. Litt J, Belfort M, Everson T, Haneuse S, Tiemeier H. Neonatal multimorbidity and the phenotype of premature aging in preterm infants. Pediatric Research 2024 View
  41. Li Y, Jin D, Zhang Y, Li W, Jiang C, Ni M, Liao N, Yuan H. Utilizing artificial intelligence to determine bone mineral density using spectral CT. Bone 2025;192:117321 View
  42. Amjad A, Szczęsna A, Błaszczyszyn M, Anwar A. Inertial measurement unit signal-based machine learning methods for frailty assessment in geriatric health. Signal, Image and Video Processing 2025;19(1) View

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
  3. Gnarra O, Amrein S, Ejtehadi M, Yoshimi T, Kato K, Paez-Granados D. Converging Clinical and Engineering Research on Neurorehabilitation V. View