Deep Learning of Electronic Health Records: Opportunities and Challenges

Abstract

Electronic health records (EHRs) have revolutionized the healthcare sector in the sense that they have substituted the majority of the paperwork in the recording of patient records in different aspects, like diagnoses, medication, laboratory results, and clinical notes. The growing EHR information, as well as the creation of the deep learning architecture, have created unprecedented opportunities to use this information to make actionable insights regarding the prediction of diseases, their diagnosis, and individualized treatment recommendations. It is a generalized survey touching the deep learning applications sphere in EHR analysis, which is likely to have revolutionary opportunities in the form of early-stage disease diagnosis, clinical decision-making, and automatic medical records. Simultaneously, critical obstacles such as the heterogeneity of the data, privacy, the lack of interpretability, and complexities in the process of integrating are addressed. Those designs considered are state-of-the-art, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, convolutional neural networks (CNNs), transformer-based models, and graph neural networks (GNNs) applied to structured and sequential EHR data. New developments in federated learning with privacy protection, explainable artificial intelligence (XAI) with clinical interpretability and multi-modal learning approaches based on imaging, genomics, and clinical text are also highlighted. The paper would provide a comprehensive system of understanding and implementing deep learning solutions into clinical practice for healthcare organizations, researchers, and practitioners and reflect on the ethical, regulatory, and practical factors.