Machine Learning Techniques for Biomedical Natural Language Processing: A Comprehensive Review

Abstract

The widespread use of electronic health records (EHR) systems in health care provides a large amount of real-world data, leading to new areas for clinical research. Natural language processing (NLP) techniques have been used as an artificial intelligence strategy to extract information from clinical narratives in electronic health records since they include a great amount of valuable clinical information. However, in a free-form text such as electronic health records, many clinical data are still hidden in a clinical narrative format. Therefore, the performance of biomedical NLP techniques is required to unlock the full potential of EHR data to convert a clinical narrative text automatically into structured clinical data. In this way, biomedical NLP applications can be used to direct clinical decisions, identify medical problems, and effectively postpone or avoid the occurrence of a disease. This review discusses the current literature on the secondary use of electronic health record data for clinical research on chronic diseases and addresses the potential, challenges, and applications of biomedical NLP techniques. We review some of the biomedical NLP methods and systems used over EHRs and give an overview of machine learning and deep learning methodologies used to process EHRs and improve the understanding of the patient’s clinical records and the prediction of chronic diseases risk, providing a great chance to extract previously unknown clinical information. Moreover, this review summarizes the utilizing of Deep Learning and Machine Learning techniques in biomedical NLP tasks based on chronic diseases related EHR data. Finally, this review presents the future trends and challenges in the biomedical NLP.

Keywords

• Artificial intelligence (AI)
• clinical information
• deep learning
• electronic health records (EHR)
• machine learning
• natural language processing (NLP)