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Editorial
. 2021 Oct 21;9(10):e23898.
doi: 10.2196/23898.

Health Natural Language Processing: Methodology Development and Applications

Tianyong Hao  1 , Zhengxing Huang  2 , Likeng Liang  1 , Heng Weng  3 , Buzhou Tang  4
Affiliations
Editorial

Health Natural Language Processing: Methodology Development and Applications

Tianyong Hao et al. JMIR Med Inform. .

Abstract

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".

Keywords: application; health care; methodology; natural language processing; unstructured text.

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Conflict of interest statement

Conflicts of Interest: None declared.

References

    1. Chen X, Xie H, Wang F, Liu Z, Xu J, Hao T. A bibliometric analysis of natural language processing in medical research. BMC Med Inform Decis Mak. 2018 Mar 22;18(Suppl 1):14. doi: 10.1186/s12911-018-0594-x. https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-0... 10.1186/s12911-018-0594-x - DOI - DOI - PMC - PubMed
    1. Hao T, Rusanov A, Boland MR, Weng C. Clustering clinical trials with similar eligibility criteria features. J Biomed Inform. 2014 Dec;52:112–20. doi: 10.1016/j.jbi.2014.01.009. https://linkinghub.elsevier.com/retrieve/pii/S1532-0464(14)00011-2 S1532-0464(14)00011-2 - DOI - PMC - PubMed
    1. Velupillai S, Mowery D, South BR, Kvist M, Dalianis H. Recent advances in clinical natural language processing in support of semantic analysis. Yearb Med Inform. 2015 Aug 13;10(1):183–93. doi: 10.15265/IY-2015-009. http://www.thieme-connect.com/DOI/DOI?10.15265/IY-2015-009 me2015-009 - DOI - PMC - PubMed
    1. Kalyan KS, Sangeetha S. SECNLP: a survey of embeddings in clinical natural language processing. J Biomed Inform. 2020 Jan;101:103323. doi: 10.1016/j.jbi.2019.103323. https://linkinghub.elsevier.com/retrieve/pii/S1532-0464(19)30243-6 S1532-0464(19)30243-6 - DOI - PubMed
    1. Shen F, Liu S, Fu S, Wang Y, Henry S, Uzuner O, Liu H. Family history extraction from synthetic clinical narratives using natural language processing: overview and evaluation of a challenge data set and solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing (OHNLP) Competition. JMIR Med Inform. 2021 Jan 27;9(1):e24008. doi: 10.2196/24008. https://medinform.jmir.org/2021/1/e24008/ v9i1e24008 - DOI - PMC - PubMed

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