Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population
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...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2023-12-01
|
Series: | BMC Geriatrics |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12877-023-04523-8 |
_version_ | 1797388045762166784 |
---|---|
author | Kyu-Nam Heo Jeong Yeon Seok Young-Mi Ah Kwang-il Kim Seung-Bo Lee Ju-Yeun Lee |
author_facet | Kyu-Nam Heo Jeong Yeon Seok Young-Mi Ah Kwang-il Kim Seung-Bo Lee Ju-Yeun Lee |
author_sort | Kyu-Nam Heo |
collection | DOAJ |
description | 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, respectively. The outcome of interest was serious FRIs, which we defined operationally as incidents necessitating an emergency 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 sensitivity 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 community-dwelling older adults. With prospective validation, this model could facilitate targeted fall prevention strategies in primary care or community-pharmacy settings. |
first_indexed | 2024-03-08T22:35:02Z |
format | Article |
id | doaj.art-20b9b06c4538442ead10583f9c9057ac |
institution | Directory Open Access Journal |
issn | 1471-2318 |
language | English |
last_indexed | 2024-03-08T22:35:02Z |
publishDate | 2023-12-01 |
publisher | BMC |
record_format | Article |
series | BMC Geriatrics |
spelling | doaj.art-20b9b06c4538442ead10583f9c9057ac2023-12-17T12:29:17ZengBMCBMC Geriatrics1471-23182023-12-0123111010.1186/s12877-023-04523-8Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older populationKyu-Nam Heo0Jeong Yeon Seok1Young-Mi Ah2Kwang-il Kim3Seung-Bo Lee4Ju-Yeun Lee5College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National UniversityCollege of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National UniversityCollege of Pharmacy, Yeungnam UniversityDepartment of Internal Medicine, Seoul National University Bundang HospitalDepartment of Medical Informatics, Keimyung University School of MedicineCollege of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National UniversityAbstract 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, respectively. The outcome of interest was serious FRIs, which we defined operationally as incidents necessitating an emergency 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 sensitivity 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 community-dwelling older adults. With prospective validation, this model could facilitate targeted fall prevention strategies in primary care or community-pharmacy settings.https://doi.org/10.1186/s12877-023-04523-8FallFall-related injuryOlder adultsMachine-learningPrediction modelClaims data |
spellingShingle | Kyu-Nam Heo Jeong Yeon Seok Young-Mi Ah Kwang-il Kim Seung-Bo Lee Ju-Yeun Lee Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population BMC Geriatrics Fall Fall-related injury Older adults Machine-learning Prediction model Claims data |
title | Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population |
title_full | Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population |
title_fullStr | Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population |
title_full_unstemmed | Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population |
title_short | Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population |
title_sort | development and validation of a machine learning based fall related injury risk prediction model using nationwide claims database in korean community dwelling older population |
topic | Fall Fall-related injury Older adults Machine-learning Prediction model Claims data |
url | https://doi.org/10.1186/s12877-023-04523-8 |
work_keys_str_mv | AT kyunamheo developmentandvalidationofamachinelearningbasedfallrelatedinjuryriskpredictionmodelusingnationwideclaimsdatabaseinkoreancommunitydwellingolderpopulation AT jeongyeonseok developmentandvalidationofamachinelearningbasedfallrelatedinjuryriskpredictionmodelusingnationwideclaimsdatabaseinkoreancommunitydwellingolderpopulation AT youngmiah developmentandvalidationofamachinelearningbasedfallrelatedinjuryriskpredictionmodelusingnationwideclaimsdatabaseinkoreancommunitydwellingolderpopulation AT kwangilkim developmentandvalidationofamachinelearningbasedfallrelatedinjuryriskpredictionmodelusingnationwideclaimsdatabaseinkoreancommunitydwellingolderpopulation AT seungbolee developmentandvalidationofamachinelearningbasedfallrelatedinjuryriskpredictionmodelusingnationwideclaimsdatabaseinkoreancommunitydwellingolderpopulation AT juyeunlee developmentandvalidationofamachinelearningbasedfallrelatedinjuryriskpredictionmodelusingnationwideclaimsdatabaseinkoreancommunitydwellingolderpopulation |