Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population

Aug 5, 2026

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Falls affect over 25% of older adults each year, making effective prevention a major public health priority. This study develops and validates a machine-learning prediction model for serious fall-related injuries (FRIs) among community-dwelling older adults, incorporating multiple medication-related factors. Development and validation use nationwide patient sample data, defining serious FRIs as events requiring emergency department visits or hospital admission based on injury diagnostic codes.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

Background

Methods

Results

Conclusion

Introduction

Falls as a public health problem

Medication-related fall risk factors

Medication-focused fall prevention strategies

Heo et al. BMC Geriatrics (2023) 23:830 [https://doi.org/10.1186/s12877-023-04523-8](https://doi.org/10.1186/s12877-023-04523-8) BMC Geriatrics RESEARCH Open Access Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population Kyu‑Nam Heo 1, Jeong Yeon Seok1, Young‑Mi Ah2, Kwang‑il Kim3,4, Seung‑Bo Lee5* and Ju‑Yeun Lee 1* Abstract Background Falls impact over 25% of older adults annually, making fall prevention a critical public health focus. We aimed to develop and validate a machine learning‑based prediction model for serious fall‑related injuries (FRIs) among community‑dwelling older adults, incorporating various medication factors. Methods Utilizing annual national patient sample data, we segmented outpatient older adults without FRIs in the preceding three months into development and validation cohorts based on data from 2018 and 2019, respec‑ tively. The outcome of interest was serious FRIs, which we defined operationally as incidents necessitating an emer‑ gency department visit or hospital admission, identified by the diagnostic codes of injuries that are likely associated with falls. We developed four machine‑learning models (light gradient boosting machine, Catboost, eXtreme Gradient Boosting, and Random forest), along with a logistic regression model as a reference. Results In both cohorts, FRIs leading to hospitalization/emergency department visits occurred in approximately 2% of patients. After selecting features from initial set of 187, we retained 26, with 15 of them being medication‑related. Catboost emerged as the top model, with area under the receiver operating characteristic of 0 . 700, along with sen‑ sitivity and specificity rates around 65% . The high‑risk group showed more than threefold greater risk of FRIs than the low‑risk group, and model interpretations aligned with clinical intuition. Conclusion We developed and validated an explainable machine‑learning model for predicting serious FRIs in com‑ munity‑dwelling older adults. With prospective validation, this model could facilitate targeted fall prevention strate‑ gies in primary care or community‑pharmacy settings. Keywords Fall, Fall‑related injury, Older adults, Machine‑learning, Prediction model, Claims data *Correspondence: Seung‑Bo Lee [koreateam23@gmail.com](koreateam23@gmail.com)[ ](koreateam23@gmail.com)Ju‑Yeun Lee [jypharm@snu.ac.kr](jypharm@snu.ac.kr) Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[. The Creative Commons Public Domain Dedication waiver (](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[http://creativeco](http://creativeco)[mmons.org/publicdomain/zero/1.0/](mmons.org/publicdomain/zero/1.0/)) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Heo et al. BMC Geriatrics (2023) 23:830 Introduc

FAQs

What was the goal of this study on fall-related injuries?

To develop and validate a machine-learning prediction model for serious fall-related injuries among community-dwelling older adults, with an emphasis on medication factors.

How did the study define serious fall-related injuries (FRIs)?

FRIs were defined operationally as incidents requiring an emergency department visit or hospital admission, identified using diagnostic codes of injuries likely associated with falls.

Which machine-learning model performed best and how was it evaluated?

Catboost performed best, with an area under the receiver operating characteristic curve around 0.700, and sensitivity/specificity rates around 65%.

Aug 5, 2026

Upload by Maeve