Enhancing Disease Prediction with a Hybrid CNN-LSTM Framework in EHRs

Abstract

This study developed a novel hybrid deep learning framework aimed at enhancing the accuracy of disease prediction using temporal data from Electronic Health Records (EHRs). The framework integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, leveraging the strength of CNNs in extracting hierarchical feature representations from complex data and the capability of LSTMs in capturing long-term dependencies in temporal information. An empirical investigation on real-world EHR datasets revealed that, compared to Support Vector Machine (SVM) models, standalone CNNs, and LSTMs, this hybrid deep learning network demonstrated significantly higher prediction accuracy in disease prediction tasks. This research not only advances the performance of predictive models in the health data analytics domain but also underscores the importance of adopting and further developing advanced deep learning technologies to address the complexity of modern medical data. Our findings advocate for a shift towards integrating complex neural network architectures in developing predictive models, potentially offering avenues for more personalized and proactive disease management and care, thereby setting new standards for future health management practices.