An Inpatient Fall Risk Assessment Tool: Application of Machine Learning Models on Intrinsic and Extrinsic Risk Factors

Abstract

Abstract Purpose This study aimed to identify the most impactful set of intrinsic and extrinsic fall risk factors and develop a data-driven inpatient fall risk assessment tool (FRAT). Methods The dataset used for the study comprised in-hospital fall records from 2012 to 2017. Two machine learning (ML) algorithms, Support Vector Machine (SVM) and Random Forest (RF) were utilized to predict the inpatient fall risk level. To enhance the performance of the prediction models, two approaches were implemented, including (1) feature selection to identify the optimal feature set and (2) the development of three distinct shift-wise models. Furthermore, the optimal feature sets in the shift-wise models were extracted. Results According to the results, RF outperformed SVM by reaching an accuracy, sensitivity, specificity, and AUC of 0.66, 0.74, 0.59, and 0.73, respectively, considering the full set of features. The performance of the models was further improved (by 3%-5%) by conducting a feature selection process for both RF and SVM models. Specifically, the RF model achieved an accuracy of 0.69 while considering the optimal set of predictors. Moreover, the shift-wise RF models demonstrated higher accuracies (by 4%-10%) compared to the same model using a full feature set. Conclusion This study's outcome confirms ML models' compelling capability in developing an inpatient FRAT while considering intrinsic and extrinsic factors. The insight from such models could form a foundation to (1) monitor the inpatients’ fall risk, (2) identify the major factors involved in inpatient falls, and (3) create subject-specific self-care plans.