Deep Learning Methods for Electronic Health Records-Based on Early Chronic Diseases Detection

Abstract

Electronic Health Records (EHR) possess extensive patient information to help healthcare providers find chronic diseases during their initial stages. Deep learning has established itself as an efficient method for processing elaborate patterns in EHR information which results in improved disease diagnosis systems and better prognosis predictions. This research investigates the usage of RNNs and CNNs and transformer-based systems for detecting chronic diseases at their early stages. This paper describes the methods used for pre-processing data while explaining feature extraction approaches in addition to presenting model performance evaluation methods. The research confirms deep learning techniques achieve superior diagnostic forecast capability by simultaneously controlling misdiagnosis along with instant medical assistance availability. The modeling difficulties about privacy along with computational requirements and interpretability challenges will not halt the healthcare transformation which deep learning enables for detecting early diseases and delivering personalized medical care.