This paper reviews the growing use of deep learning in analyzing EHR data to forecast disease outcomes and improve diagnostic accuracy. EHRs encompass a wealth of patient information—including clinical notes, diagnoses, and demographic data—which makes them ideal for training predictive algorithms. However, their high dimensionality, heterogeneity, and limited public availability pose significant challenges.
The review synthesizes state-of-the-art deep learning architectures and the online databases currently available for EHR-based research. It categorizes models based on usability and features, aiming to help researchers choose the most effective tools for their objectives. The paper also highlights how end-to-end learning models have become increasingly viable, thanks to recent advances in AI.
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