Building Robust Predictive Models for Patient Risk Management: A Machine Learning Approach to EHR Data

Abstract

Machine learning (ML) in healthcare is the domain that can be applied in one of the areas thus risk management of a patient can improve the clinical decision-making process. The huge volume of patient data made available by Electronic Health Records (EHRs) can be analyzed with the aid of advanced ML models and lead to predictions concerning patient risks, i.e., the risk of readmission, complications, or death. The purpose of the study is developing promising predictive models based on the ML algorithm to enhance the management of patient risks. We use many of them in terms of analyzing EHR data and identifying the important predictor of patient outcomes, including Random Forests (RF), Support Vector Machines (SVM), Neural Networks (NN). The results indicate that Random Forests achieved the best results in accuracy and interpretability, whereas Neural Networks are expected to show the highest results in predicting complex patterns among other models. The present paper explains the implications of ML in risk management of patients and recommends that such models are likely to result in more efficient allocation of resources and even superior outcomes on patient care. We underline the importance of the preprocessing of the data carried out and also the ethical consequence of ML models application into the actual clinical practice. Machine learning, patient risk management, electronic health record, predictive modelling, random forest and neural networks and healthcare analytics Machine learning, patient risk management, electronic health record, predictive modelling, random forest and neural networks also healthcare analytics.