Deep Learning Techniques for Electronic Health Record (EHR) Analysis

Abstract

Data is gathered from millions of patients across various healthcare organizations could be framed as electronic health record (EHR). It comprises of data in various forms that include laboratory results, diagnosis reports, medical images, demographic information about patient, and clinical notes. Transformation in the health care could be done with the help of deep learning techniques since these techniques outperform than the conventional machine learning methods. In addition, a huge and complex dataset gets generated day by day, and thus, it enables the need for the deployment of deep learning models. It is examined that the clinical decision in certain fields that vary from analysis of medical research issues and to suggest as well as prioritize the treatments in order to detect abnormalities, and in addition, the identification of genomic markers in tissue samples is done. Deep learning is applied in EHR system to derive patient representation to provide enhanced clinical predictions, and augments clinical decision systems. Moreover, it is applied in order to detect the disease in the earlier stages, determining the clinical risk, forecasting the future need of regular checkups and in addition prediction of hospitalization in the near future if required. According to statistical report, the deep learning software market is estimated to reach 180 million US dollars in size by 2020. The availability of a huge amount of clinical information particularly EHR has stimulated the growth of deep learning techniques which assist in rapid analysis of patient’s data. Nowadays, EHR is being incorporated with deep learning techniques and tools into EHR systems which provide deep insights for health outcomes. The market for EHR deep learning tools is predicted to exceed $34 billion by the mid of 2020s, motivated massively by an emerging desire to automate tasks and provide deeper insights into clinical issues. The global health IT market is anticipated to be worth a surprising $223.16 billion by 2023, driven in part by deep learning. EHR is a thriving research domain for deep learning researchers since it helps to achieve higher accuracies in medical conditions. Clinical information could be extracted by applying deep learning techniques. The extraction is of various types, namely retrieval of single idea, extracting the association, time-based extraction, and advancement of abbreviation. In EHR representation learning process, concept and patient representations are included to obtain detailed analysis and decisive predictive functionalities. Outcome prediction in deep EHR is categorized as static and temporal prediction to predict patient outcomes. The clinical deep learning is more interpretable under the category of maximum activation, constraints, qualitative clustering, and mimic learning in EHR analysis. The objective of this chapter is to provide an insight into the digital transformation of EHR through deep learning techniques. It discusses the deep learning framework and challenges that occur during the development of deep learning models for EHR. Moreover, it also focuses on the deep learning prediction techniques for various diseases and recent advancements on deep learning techniques in the aspect of providing precise medicine and future generation health care.