However, like most deep learning approaches, RNNs are prone to overfitting when labeled training data is scarce, and often require careful and computationally expensive hyper-parameter tuning effort. ![]() With various medical parameters being recorded over a period of time in EHR databases, recurrent neural networks (RNNs) can be an effective way to model the sequential aspects of EHR data and, in turn, enable applications in diagnoses, mortality prediction, and estimating length of stay. As a result, there has been a rapid growth in the applications of deep learning to various clinical prediction tasks from electronic health records, e.g., Doctor AI for medical diagnosis, Deep Patient to predict future diseases in patients, and DeepR to predict unplanned readmission after discharge. On the other hand, deep learning approaches enable end-to-end learning without the need of hand-crafted and domain-specific features, and have recently produced promising results for various clinical prediction tasks. ![]() ![]() Traditional machine learning approaches often require careful domain-specific feature engineering to achieve good prediction performance. Electronic health records (EHR) consisting of the medical history of patients are useful in various clinical applications such as diagnosis and recommending medicine.
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