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With the rapid growth of information technology, there is a great need for the processing of massive health and medical data utilizing advanced information technologies. A large amount of valuable data exists in natural text such as free diagnosis text, discharge summary, online health discussions, eligibility criteria of clinical trials, etc. Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) language texts. NLP aims to provide a computer program with the ability to process and understand unstructured texts. In the area of health, NLP can assist medical decision making by automatically analyzing the commonalties and differences in massive text data and recommending appropriate actions on behalf of domain experts. State-of-the-art NLP techniques have proven to be useful in dealing with the information overload problem in the health and medical domain, such as patient notes aggregation and summarization, treatment analysis, information extraction and retrieval of massive discharge summaries, semantic understanding of patient queries, etc.

To that end, the China Conference on Health Information Processing (CHIP) proposes the Health Natural Language Processing theme issue to contribute to the development of interdisciplinary research. CHIP is the annual symposium of the Chinese Information Processing Society of China (CIPS) Technical Committee of Medical, Health and Biological Information Processing. CHIP is a leading international conference specializing in health information processing in China. It serves as a primary forum for researchers and practitioners from academia, industry, and government worldwide to share their ideas, research results, and experiences for further promotion in the fields. The previous CHIP conferences were successfully held in 2015, 2016, 2017, 2018, and 2019. In the last three conferences, experts from the United States, Germany, France, United Kingdom, Japan, Australia, China, etc. shared their research results, experiences, and most recent achievements, making the conferences a big success. All the submitted papers were carefully reviewed following the commonly used international review standard.

CHIP will select the best-scored papers and recommend them for the theme issue. In addition, authors not attending CHIP are invited to submit papers fitting with the theme directly to JMIR Medical Informatics. In this issue, articles regarding the use of technologies, methodologies, applications for NLP and the health/medical sector, and viewpoints/reviews are welcome. The theme issue will be expected to showcase various new developments in these areas. Authors are encouraged to submit high-quality original research articles, mainly describing original research and presenting results that advance our understanding of the field.

Potential topics include but are not limited to the following:

• NLP of biomedical, clinical, or social web data (such as literature, EHRs, clinical trials, social media about health care, etc.)
• Health information retrieval and extraction
• Text mining or machine learning on biomedical, clinical, or social web data (such as literature, EHRs, clinical trials, social media about health care, etc.)
• Text corpora and annotations on biomedical, clinical, or social web data (such as literature, EHRs, clinical trials, social media about health care, etc.)
• Medical ontologies
• Novel tools and ontologies for biomedical, clinical, or social web data interpretation and visualization
• Health care knowledge representation and reasoning
• Clinical decision support and informatics
• Mobile technologies for health care applications
• Protein structure and function prediction from text based on machine learning methods
• Advanced machine learning methods and their applications to medical and health text data
• NLP techniques for the personalization of medicine
• NLP-assisted health information aggregation, abstraction, and summarization
• Innovative NLP methods for capturing patients’ intentions from text
• Question answering technologies for health application
• Innovative NLP systems for mobile environment
• Health and medical knowledge graphs
• Trends and challenges in health and medical NLP

Please prepare your manuscript with the instructions found here: https://www.jmir.org/content/author-instructions.

Submissions should be sent through the online system at https://medinform.jmir.org/author. Authors should choose the section ‘Theme issue 2021: Health Natural Language Processing and Applications’ when submitting papers (see FAQ article on how to submit to a theme issue: https://support.jmir.org/hc/en-us/articles/115001429168-How-do-I-submit-to-a-theme-issue-).

Invited/accepted articles with corresponding authors from institutions that are not JMIR institutional members are subject to the regular JMIR Article Processing Fee (APF). For this theme issue, the APF is discounted by 20%. 

Please see the fee schedule for details:https://medinform.jmir.org/fees/article-processing-fees

Guest Editors

Tianyong Hao, South China Normal University, China; haoty@m.scnu.edu.cn;Buzhou Tang, Harbin Institute of Technology, China; tangbuzhou@hit.edu.cn;Zhengxing Huang, Zhejiang University, China; zhengxinghuang@zju.edu.cn