Deep Learning Techniques for Early Detection of Chronic Diseases Using Electronic Health Records

Abstract

Disease early diagnosis is especially important for chronic diseases to achieve a positive outcome for patients with such ailments and minimal costs for the treatment. The possibility of early diagnosis and intervention based on the large amount of patient data that can be found in EHRs is already quite high. In this research, deep learning approaches will be used to screen EHR databases for early identification of Chronic diseases including diabetes, hypertension, and cardiovascular diseases. In this talk, based on the CNNs, RNNs, and transformer models, we illustrate how these approaches can explore patterns and risk factors of chronic diseases compared to conventional methodologies. These predictions imply that deep learning trained on large EHR can be used to identify tendencies for chronic diseases’ emergence, thus allowing for timely diagnosis and first-person treatment strategies development. Additionally, the work underscores the need for the fundamental steps of data preprocessing, feature selection, and model interpretability to maintain the accuracy and the ethical application of these predictive models among clinicians.