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

Decision Support for Health Professionals (2381) Clinical Information and Decision Making (3937) mHealth in a Clinical Setting (1232) Machine Learning (3368) Falls and Emergency Detection with Sensors and other Technologies (83)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/75958, first published .
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Machine Learning–Based Prediction of In-Hospital Falls in Adult Inpatients: Retrospective Observational Multicenter Study

Machine Learning–Based Prediction of In-Hospital Falls in Adult Inpatients: Retrospective Observational Multicenter Study

Authors of this article:

Takuya Nishino1 Author Orcid Image ;   Kotone Matsuyama1, 2 Author Orcid Image ;   Yasuo Miyagi3, 4 Author Orcid Image ;   Nari Tanabe5 Author Orcid Image ;   Fumiko Yamaguchi3 Author Orcid Image ;   Hiroki Ito3 Author Orcid Image ;   Shizuka Soh3 Author Orcid Image ;   Ayako Yano6 Author Orcid Image ;   Masako Mizuno7 Author Orcid Image ;   Katsuhito Kato8 Author Orcid Image ;   Hiroshige Jinnouchi8 Author Orcid Image ;   Chol Kim6, 9 Author Orcid Image ;   Yosuke Ishii4 Author Orcid Image ;   Hiroki Yamaguchi3, 10 Author Orcid Image ;   Yukihiro Kondo1, 11 Author Orcid Image
Article Authors Cited by (7) Tweetations (1) Metrics

Journals

  1. Nishino T, Kubota Y, Miyagi Y, Tanabe N, Yamaguchi F, Ito H, Soh S, Yano A, Mizuno M, Kim C, Ishii Y, Kondo Y, Asai K, Liampas I. Association between hypnotic medication use and in-hospital falls among older adults: A multicenter landmark analysis. PLOS One 2026;21(6):e0351299 View
  2. Lee Y, Hsu Y, Lai S, Lin C, Lin C, Xu J. Admission clinical and functional assessments for predicting inpatient fall risk using machine learning models. DIGITAL HEALTH 2026;12 View
  3. Mordiffi S, Guo X, Goh M, Ngiam K, Wong N, Chua J, Furqan M, Chew H. Development and Validation of an Interpretable Machine Learning Model for Inpatient Fall Risk Using Electronic Health Record Data. Nursing Reports 2026;16(8):283 View
  4. Schulte-Althoff M, Krappen P, Bießmann F, Jäger S, Gubser R, Hauss A, Balzer F, Kilgus T, Fürstenau D. Explainable SHAP-space fall risk profiling from electronic health records for inpatient prevention planning. npj Health Systems 2026;3(1) View
  5. Wang S, Zhang X, Qin Y, Xu X, Du L, Xu P, Yu J, Li Y, Yang L. Development and validation of a machine learning-based model for predicting fall-related injury risk in hospitalized patients. International Journal for Quality in Health Care 2026;38(3) View
  6. Nishino T, Kubota Y, Miyagi Y, Tanabe N, Yamaguchi F, Ito H, Soh S, Yano A, Mizuno M, Kim C, Ishii Y, Asai K. Association between early in-hospital physiological and functional changes and fall risk in older adults: a retrospective multicenter cohort study. European Geriatric Medicine 2026 View

Conference Proceedings

  1. Karapintzou E, Tsakanikas V, Bozios K, Exarchos T, Nairn B, Pavlou M, Bamiou D, Fotiadis D. 2026 IEEE 39th International Symposium on Computer-Based Medical Systems (CBMS). Subgroup Analysis for Risk of Fall Correlation Using the UK Biobank Dataset View