Paper Type
Complete
Abstract
The increasing trend of patients’ falls-related impairments in acute care hospitalization and the consequent implications for quality care and cost of care have made it prudent to develop techniques for rapid estimation of fall risks on admission. We develop a framework that relies on Extra Tree Classifier (ETC) with class balanced features to model fall risks on admission using risk scores and removing redundant clinical and psychosocial characteristics via multicollinearity and significance testing. The model predicts fall risks on admission to an accuracy of 96.76% and has a higher accuracy of 3.63%-39.32% when compared to Logistics Regression (LR), Linear Discriminant Analysis (LDA), decision tree classifier(DTC), Quadratic Discriminant Analysis(QDA), K Nearest Neighbour(KNN), Support Vector Machine (SVM), Ridge model (RCV) and Artificial Neural Network (ANN). Due to the effectiveness of this model, it is expected that the multifactorial considerations will culminate in cost-effective management and better patient experience.
Recommended Citation
Ossai, Chinedu; O’Connor, Louise; and Wickramasinghe, Nilmini, "Predicting Fall Risks Vulnerability with Inpatient Data in Acute Care Hospitalization" (2020). AMCIS 2020 Proceedings. 1.
https://aisel.aisnet.org/amcis2020/data_science_analytics_for_decision_support/data_science_analytics_for_decision_support/1
Predicting Fall Risks Vulnerability with Inpatient Data in Acute Care Hospitalization
The increasing trend of patients’ falls-related impairments in acute care hospitalization and the consequent implications for quality care and cost of care have made it prudent to develop techniques for rapid estimation of fall risks on admission. We develop a framework that relies on Extra Tree Classifier (ETC) with class balanced features to model fall risks on admission using risk scores and removing redundant clinical and psychosocial characteristics via multicollinearity and significance testing. The model predicts fall risks on admission to an accuracy of 96.76% and has a higher accuracy of 3.63%-39.32% when compared to Logistics Regression (LR), Linear Discriminant Analysis (LDA), decision tree classifier(DTC), Quadratic Discriminant Analysis(QDA), K Nearest Neighbour(KNN), Support Vector Machine (SVM), Ridge model (RCV) and Artificial Neural Network (ANN). Due to the effectiveness of this model, it is expected that the multifactorial considerations will culminate in cost-effective management and better patient experience.
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