Improving Health Care Analytics: LSTM Networks for Enhanced Risk Assessment

Abstract

Accurate risk assessment and informed decision-making are essential for effective healthcare delivery. The increasing availability of electronic health records (EHR) data, coupled with advancements in deep learning techniques, presents an opportunity to leverage these resources for improving risk assessment and decision-making processes. This research paper explores the application of Long Short-Term Memory (LSTM) networks, a type of recurrent neural network, with EHR datasets to enhance risk assessment and decision-making in health care analytics. The paper specially focuses on predicting adverse events, disease progression, and treatment outcomes. The paper begins by highlighting the challenges faced in risk assessment and decision-making in healthcare, emphasizing the need for robust and efficient methods to handle the vast and complex EHR data. In this research work we used the evaluation metrics (accuracy, precision, recall, F1-score, AUC-ROC, AUC-PR, and MSE) for each fold of the 10-fold cross-validation experiment. The LSTM model demonstrated superior performance with an average accuracy of 87%, a precision of 89%, and a recall of 86%. The model also achieved an F1-score of 87%, an AUC-ROC of 0.92, and an AUC-PR of 0.89, indicating strong discriminative ability and precision across different probability thresholds. These findings suggest that LSTM networks hold considerable promise for improving clinical decision-making and patient outcomes in healthcare settings.

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