Health Natural Language Processing: Methodology Development and Applications

• Published in: JMIR medical informatics Vol. 9; no. 10; p. e23898
• Main Authors: Hao, Tianyong, Huang, Zhengxing, Liang, Likeng, Weng, Heng, Tang, Buzhou
• Format: Journal Article
• Language: English
• Published: Toronto JMIR Publications 21.10.2021
• Subjects: Artificial intelligence; Clinical trials; Datasets; Decision making; Deep learning; Electronic health records; Fraud; Health informatics; Knowledge representation; Language; Lung cancer; Machine learning; Medical records; Medical research; Natural language processing; Neural networks; Semantics; Social networks; Subject specialists; Traditional Chinese medicine; China
• ISSN: 2291-9694; 2291-9694
• DOI: 10.2196/23898

With the rapid growth of information technology, the necessity for processing substantial amounts of health data using advanced information technologies is increasing. A large amount of valuable data exists in natural text such as diagnosis text, discharge summaries, online health discussions, and eligibility criteria of clinical trials. Health natural language processing, as an interdisciplinary field of natural language processing and health care, plays a substantial role in a wide scope of both methodology development and applications. This editorial shares the most recent methodology innovations of health natural language processing and applications in the medical domain published in this JMIR Medical Informatics special theme issue entitled "Health Natural Language Processing: Methodology Development and Applications".