A Machine Learning-Based Fall Risk Assessment Model for Inpatients

Chia Hui Liu, Ya Han Hu, Yu Hsiu Lin

研究成果: 雜誌貢獻期刊論文同行評審

摘要

Falls are one of the most common accidents among inpatients and may result in extended hospitalization and increased medical costs. Constructing a highly accurate fall prediction model could effectively reduce the rate of patient falls, further reducing unnecessary medical costs and patient injury. This study applied data mining techniques on a hospital's electronic medical records database comprising a nursing information system to construct inpatient-fall-prediction models for use during various stages of inpatient care. The inpatient data were collected from 15 inpatient wards. To develop timely and effective fall prediction models for inpatients, we retrieved the data of multiple-time assessment variables at four points during hospitalization. This study used various supervised machine learning algorithms to build classification models. Four supervised learning and two classifier ensemble techniques were selected for model development. The results indicated that Bagging+RF classifiers yielded optimal prediction performance at all four points during hospitalization. This study suggests that nursing personnel should be aware of patients' risk factors based on comprehensive fall risk assessment and provide patients with individualized fall prevention interventions to reduce inpatient fall rates.

原文???core.languages.en_GB???
頁(從 - 到)450-459
頁數10
期刊CIN - Computers Informatics Nursing
39
發行號8
DOIs
出版狀態已出版 - 2021

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