A Machine Learning–Based Fall Risk Assessment Model for Inpatients

Abstract

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.

Development and validation of interpretable machine learning models for inpatient fall events and electronic medical record integration

“…Patients in this study who experienced falls tended to be older, stayed in the hospital longer, and took more medications. This result is consistent with the characteristics of patients at risk for falls, as in previous studies 14,18 . All these characteristics were used as significant predictors.…”

“…Medication and nursing intervention was important predictors consistent with previous study 18 . Lower limb weakness and dysuria was the highest predictor in previous study 14,22 , assist toileting and ambulation nursing interventions were found to be high predictor, which was similarly consistent. Another study shows admission data was high in feature importance, but this study inputted more variables to reflect specific patient conditions.…”

“…Another study shows admission data was high in feature importance, but this study inputted more variables to reflect specific patient conditions. 14 . Our study developed a model by including not only formally reported falls but also falls that were only recorded in nurses' clinical notes.…”

“…Advances in data science have contributed to the development of accurate fall risk prediction models; other studies created ensemble models or extreme gradient boosting models and identified significant predictors such as low self-care ability, sleep disorders, and medication use [ 13 , 14 ]. Though with acceptable accuracy, previous machine learning (ML) studies have limitations in their explainability for clinicians and nurses, which is also called the “black box problem.” Another challenge is that previous models have not been well-prepared for electronic medical record (EMR) integration for practical application [ 15 , 16 ].…”

A model for predicting fall risks of hospitalized elderly in Taiwan-A machine learning approach based on both electronic health records and comprehensive geriatric assessment

“…With advancing technology and improved medical informatics, some researchers predicted falls in hospitalized patients based on electronic health records (EHR), but data from HER also have some limitations (30,31). Since many risk factors have been found and the evolution of computer science and artificial intelligence, many scientists would like to predict falls by means of machine learning (32)(33)(34)(35) (Table 1). However, most datasets consisted of relatively healthy people, or young people (36).…”