الفهرس 
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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 EHRs since they include a great amount of valuable clinical information. However, in free-form text such as EHRs, 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 HER data to convert a clinical narrative text automatically into structured clinical data. In this way, BNLP applications can be used to direct clinical decisions, identify medical problems, and effectively postpone or avoid the occurrence of a disease. This thesis discusses the current literature on the secondary use of EHR data for clinical research on chronic diseases and addresses the potential, challenges, and applications of biomedical NLP techniques. This thesis reviews some of the biomedical NLP methods and systems used over EHRs and gives 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 disease risk, providing a great chance to extract previously unknown clinical information. Moreover, this thesis summarizes the utilization of Deep Learning and Machine Learning techniques in biomedical NLP tasks based on chronic diseases related EHR data and presents the future trends and challenges in
biomedical NLP.