JMIR MEDICAL INFORMATICS

Hao et al

Editorial

Health Natural Language Processing: Methodology Development
and Applications
Tianyong Hao1, PhD; Zhengxing Huang2, PhD; Likeng Liang1, PhD; Heng Weng3, PhD; Buzhou Tang4, PhD
1School of Computer Science, South China Normal University, Guangzhou, China

2

College of Computer Science and Technology, Zhejiang University, Guangzhou, China

3

The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China

4

School of Computer Science and Technology, Harbin Institute of Technology Shenzhen, Shenzhen, China

Corresponding Author:
Buzhou Tang, PhD
School of Computer Science and Technology
Harbin Institute of Technology Shenzhen
L1819, Harbin Institute of Technology Campus, Xili University Town, Nanshan
Shenzhen, 518055
China
Phone: 86 0755 26033182
Email: tangbuzhou@gmail.com

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 aninterdisciplinary 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".
(JMIR Med Inform 2021;9(10):e23898) doi: 10.2196/23898
KEYWORDS
health care; unstructured text; natural language processing; methodology; application

Introduction
Text data in an unstructured format widely exists in the medical
domain, such as diagnosis records, operation records, discharge
summaries, eligibility criteria of clinical trials, social media
comments, online health discussions, and medical publications.
Natural language processing (NLP) is a field of computerscience, artificial intelligence, and computational linguistics
concerned with the interactions between computers and human(natural) language texts. NLP aims to provide computer
programs with the ability to process and understand unstructured
text data. In the health arena, NLP techniques have been shown
to be useful in dealing with information overload in the healthand medical domain (eg, aggregation and summarization ofpatient notes, treatment analysis, information extraction andretrieval from massive discharge summaries, and semantic
understanding of patient queries) [1]. NLP may also be applied
for assisting medical decision-making by automatically
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analyzing the commonalities and differences of a large amountof text data and recommending appropriate actions on behalfof domain experts [2].
Health NLP, as an interdisciplinary field of NLP and healthcare, focuses on the methodology development of NLP and its
applications in health care. It facilitates the analysis of thecommonalities and differences of large amounts of text data
and recommends appropriate actions on behalf of domain
experts to assist medical decision-making. In general, it plays
an essential role in processing various types of health text dataand supports health applications to improve health careefficiency and efficacy.
With the increasing attention on this research field, there are
more and more developments related to health NLP. Velupillai
et al [3] shared the recent advances of health NLP in support ofsemantic analysis, covering the development of efficientmethods for health corpus annotation/deidentification and the
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leverage of NLP for clinical utility including NLP infrastructure
for a clinical use case. Kalyan and Sangeetha [4] investigatedthe embeddings in health NLP for text representation in deeplearning–based NLP tasks in clinical domains. The National
NLP Clinical Challenges/Open Health Natural Language
Processing (OHNLP) Competition [5] is held for family history
extraction from synthetic clinical narratives using NLP. A
number of health NLP tools and systems were also developed.
For example, OHNLP released a catalog of clinical NLP
software and provides interfaces to simplify the interaction of
NLP systems [6]. Typically, Apache cTAKES [7], as an NLP
system for extraction of information from electronic medical
record clinical free text, aimed to integrate best-of-breed
annotators, providing a world-class NLP system for accessing
clinical information within the free text.

Methods
Health NLP covers a wide scope of methodology research
including topics about methodology research such as NLP
models for medical or social web data (eg, literature, EHRs,
clinical trials, and social media about health care) processing;health information retrieval and extraction; NLP-assisted healthinformation aggregation, abstraction, and summarization;
machine learning–based text mining methods for health care;health care knowledge representation and reasoning; heath text
corpora construction and annotation; and medical ontology andhealth knowledge graphs construction.
With respect to the application of NLP methods to the healthcare domain, health NLP contains NLP techniques for medicine
personalization; question answering technologies for healthapplications; novel tools for medical, clinical, or social web
data interpretation and visualization; innovative NLP systems
for mobile environments for health care applications; NLP forclinical decision support and informatics; and applications ofadvanced NLP methods in clinical practice.
We reviewed 10 published articles in the JMIR Medical
Informatics theme issue Health Natural Language Processing:
Methodology Development and Applications to share the recent
developments of the studies, from methodology research toapplications.

Results
Medical Information Extraction
Medical information extraction is a key technology that supports
the development of medical informatics. Zhang et al [8]developed a new Chinese electronic medical record (EMR) data
set with six types of entities and proposed a multilevel
representation learning model based on Bidirectional Encoder
Representation from Transformers (BERT) for Chinese medical
entity recognition. The experiments on the Chinese EMR data
set and China Conference on Knowledge Graph and Semantic
Computing 2018 benchmark data set showed that the proposed
method outperformed state-of-the-art methods. Automatic
relation extraction between chemicals and diseases plays animportant role in biomedical text mining. Wang et al [9]proposed an end-to-end neural network based on a graphhttps://medinform.jmir.org/2021/10/e23898

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Hao et alconvolutional network and multi-head attention. To improve
the performance, a document-level dependency graph wasconstructed to capture dependency syntactic information acrosssentences. The graph was applied to capture the feature
representation of a document-level dependency graph, while
the multi-head attention mechanism was used to learn relative
context features from different semantic subspaces. The
experiment results showed that the method achieved the best
F-score, which was superior to state-of-the-art methods. The
graph convolutional network model was effectively used for
dependency information across sentences to improve theperformance of intersentence chemical-disease extraction.
Targeted at extracting the interactions between chemicals andproteins from the biomedical literature, Wang et al [10] proposed
effectively encoding syntactic information from long text. The
method leveraged graph convolutional networks to capture
sequential information and long-range syntactic relations
between words by using the dependency structure of input
sentences. The evaluation of the ChemProt corpus showed thatthe model achieved an F-score of 65.17%, which was 1.07%
higher than that of the state-of-the-art system. The study
indicated that the graph neural network–based model could
better capture the semantic and syntactic information of thebiomedical literature sentence. Temporal information frequently
exists in the representation of the disease progress, prescription,
medication, surgery progress, or discharge summary in narrative
clinical text. The extraction and normalization of temporal
expressions can positively boost the analysis and understandingof narrative clinical texts to promote clinical research andpractice. Pan et al [11] proposed a rule-based and pattern
learning–based model for extracting and normalizing temporal
expressions from Chinese narrative clinical text. The model
consisted of three stages: extraction, classification, andnormalization. Based on a set of narrative clinical texts in
Chinese containing 1459 discharge summaries of a domestic
Grade-A Class 3 hospital, the performance of the model
achieved the performance compared with baseline methods.
The research of medical information extraction still has thechallenges of insufficient training data size, complex domain
terminology, a large proportion of noise data, and significant
inconsistency among various data types.

Heath Knowledge Graph and Its Applications
Targeted at knowledge graph embedding for semantic
representation of entities and relations, the challenge of how tolearn probability values of triplets into representation vectors
was addressed. Li et al [12] constructed a mapping function
between score value and probability, and introduced
probability-based loss of triplets into original margin-based loss
function. Compared with state-of-the-art TransX algorithms,
the proposed model performed better in all evaluation indicators.
Checking whether the medication is clinically reasonable withrespect to the diagnosis is the key to fraud, waste, and abusedetection, which is a significant yet challenging problem in thehealth insurance industry. Sun et al [13] built an automatic
method to identify the clinically suspected claims for fraud,
waste, and abuse detection by using a medical knowledge graph.
A deep learning–based method was applied to extract the entities
and relationships from knowledge sources, and a multilevel
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similarity matching method was developed for entity linking.
From 185,796 drug labels from the China Food and Drug
Administration, a medical knowledge graph containing
1,616,549 nodes and 5,963,444 edges was constructed for
identifying fraud, waste, and abuse suspects. The research ofhealth knowledge graphs still has the challenges of complex
text representation, low extract performance, and limited
knowledge graph size.

NLP Methods for Health Text Mining
Traditional Chinese medicine (TCM) has been shown to be an
efficient mode to manage advanced lung cancer, and accurate
syndrome differentiation is crucial to treatment. Liu et al [14]established five deep learning–based TCM diagnostic models
to imitate lung cancer syndrome differentiation. The models
used unstructured medical records as inputs to capitalize on
data collected for practical TCM treatment cases by lung cancer
experts. The experiment result showed the F1-score of therecurrent convolutional neural network model improved overmodels without data augment. The text-hierarchical attention
network model achieved the highest F1-score. Medical records
could be used more productively by constructing end-to-end
models to facilitate lung cancer. The classification of clinical
trial eligibility criteria texts is a fundamental and critical stepin clinical target population recruitment. Zeng et al [15]proposed an ensemble learning method that integrates the current
cutting-edge deep learning models BERT, Enhanced Language
Representation with Informative Entities, XLNet, and RoBERT.
Through a model ensemble in two layers, the study trained amodel and compared it with a list of baseline deep learningmodels on a publicly available standard data set. The results
demonstrated that the proposed ensemble learning method
outperformed a list of baseline methods. The research of NLP
methods still heavily relies on the advancement of machinelearning models.

Advanced Applications
Deidentification of clinical records, as an application, is a criticalstep in the use of electronic health records for academic research.
Zhao et al [16] investigated the usefulness of rule-based learners
in a hybrid deidentification system. A data-driven rule learnernamed transformation-based error-driven learning was integrated

Hao et al
into a hybrid system. Based on the widely used Informatics for
Integrating Biology and the Bedside deidentification data set,
the learner could offer high performance with generated rules.
After integrating the learner into an ensemble framework, theperformance achieved the best among the community. The
rule-based method thus could offer an effective contribution to
the current ensemble learning approach for the deidentificationof clinical records as a typical application in medical
informatics. An artificial intelligence–based assistive diagnostic
system is designed to diagnose multiple types of diseases thatare common in TCM based on patients’ electronic health record
notes. Zhang et al [17] developed a method to simultaneously
diagnose the disease and produce a list of corresponding
syndromes. NLP techniques using a recurrent neural networkmodel were applied to process unstructured electronic healthrecord notes to extract clinical information such as signs andsymptoms that were represented by named entities. A total of
22,984 electronic health records from Guanganmen Hospital of
the China Academy of Chinese Medical Sciences were collected
and applied to the diagnostic system. From the evaluation, 187
commonly known TCM diseases could be diagnosed, and awider range of TCM disease types was expected to be diagnosed.
The applications of NLP methods tend to be more and morewidespread in the health care domain. However, the challenges,
including the security of data, the actual needs from clinicians,
the validation of results, and user convenience, still need to besolved in the future.

Conclusion
Health NLP draws more and more attention for its essential rolein a wide scope of both methodology development andapplications. This editorial shares the most recent methodology
research of health NLP and its applications in health care by
reviewing 10 newly published articles on the JMIR Medical
Informatics theme issue Health Natural Language Processing:
Methodology Development and Applications. The research
indicates recent focuses on medical information extraction(entity, relation, temporal, and interaction extraction),knowledge graph construction and use, methods for clinicaldecision support and informatics, and NLP systems for healthcare applications in practice.

Acknowledgments
TH is supported by the National Natural Science Foundation of China (61772146) and Guangzhou Science Technology andInnovation Commission (201803010063). ZH is supported by the National Natural Science Foundation of China (61672450).
HW is supported by the National Natural Science Foundation of China (61871141). BT is supported by the following grants:
National Natural Science Foundations of China (U1813215 and 61876052); Special Foundation for Technology Research Programof Guangdong Province (2015B010131010); National Natural Science Foundation of Guangdong, China (2019A1515011158);
Guangdong Province Covid-19 Pandemic Control Research Fund (2020KZDZX1222); Strategic Emerging Industry Development
Special Fund of Shenzhen (JCYJ20180306172232154); and Innovation Fund of Harbin Institute of Technology(HIT.NSRIF.2017052).

Conflicts of Interest
None declared.

References

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1.

2.
3.

4.
5.

6.
7.
8.
9.

10.

11.

12.

13.

14.

15.

16.

17.

Hao et alChen 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 [FREE Full text] [doi: 10.1186/s12911-018-0594-x] [Medline:
29589569]
Hao T, Rusanov A, Boland MR, Weng C. Clustering clinical trials with similar eligibility criteria features. J Biomed Inform
2014 Dec;52:112-120 [FREE Full text] [doi: 10.1016/j.jbi.2014.01.009] [Medline: 24496068]
Velupillai S, Mowery D, South BR, Kvist M, Dalianis H. Recent advances in clinical natural language processing in supportof semantic analysis. Yearb Med Inform 2015 Aug 13;10(1):183-193 [FREE Full text] [doi: 10.15265/IY-2015-009][Medline: 26293867]
Kalyan KS, Sangeetha S. SECNLP: a survey of embeddings in clinical natural language processing. J Biomed Inform 2020
Jan;101:103323 [FREE Full text] [doi: 10.1016/j.jbi.2019.103323] [Medline: 31711972]
Shen F, Liu S, Fu S, Wang Y, Henry S, Uzuner O, et al. 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 [FREE Full text] [doi: 10.2196/24008] [Medline: 33502329]
Masanz J, Pakhomov SV, Xu H, Wu ST, Chute CG, Liu H. Open source clinical NLP - more than any single system. AMIA
Jt Summits Transl Sci Proc 2014;2014:76-82 [FREE Full text] [Medline: 25954581]
Apache cTAKES. URL: https://ctakes.apache.org/ [accessed 2021-02-01]
Zhang Z, Zhu L, Yu P. Multi-level representation learning for Chinese medical entity recognition: model development andvalidation. JMIR Med Inform 2020 May 04;8(5):e17637 [FREE Full text] [doi: 10.2196/17637] [Medline: 32364514]
Wang J, Chen X, Zhang Y, Zhang Y, Wen J, Lin H, et al. Document-level biomedical relation extraction using graphconvolutional network and multihead attention: algorithm development and validation. JMIR Med Inform 2020 Jul
31;8(7):e17638 [FREE Full text] [doi: 10.2196/17638] [Medline: 32459636]
Wang E, Wang F, Yang Z, Wang L, Zhang Y, Lin H, et al. A graph convolutional network-based method for chemical-protein
interaction extraction: algorithm development. JMIR Med Inform 2020 May 19;8(5):e17643 [FREE Full text] [doi:10.2196/17643] [Medline: 32348257]
Pan X, Chen B, Weng H, Gong Y, Qu Y. Temporal expression classification and normalization from Chinese narrative
clinical texts: pattern learning approach. JMIR Med Inform 2020 Jul 27;8(7):e17652 [FREE Full text] [doi: 10.2196/17652][Medline: 32716307]
Li L, Wang P, Wang Y, Wang S, Yan J, Jiang J, et al. A method to learn embedding of a probabilistic medical knowledge
graph: algorithm development. JMIR Med Inform 2020 May 21;8(5):e17645 [FREE Full text] [doi: 10.2196/17645][Medline: 32436854]
Sun H, Xiao J, Zhu W, He Y, Zhang S, Xu X, et al. Medical knowledge graph to enhance fraud, waste, and abuse detection
on claim data: model development and performance evaluation. JMIR Med Inform 2020 Jul 23;8(7):e17653 [FREE Fulltext] [doi: 10.2196/17653] [Medline: 32706714]
Liu Z, He H, Yan S, Wang Y, Yang T, Li GZ. End-to-end models to imitate traditional Chinese medicine syndrome
differentiation in lung cancer diagnosis: model development and validation. JMIR Med Inform 2020 Jun 16;8(6):e17821[FREE Full text] [doi: 10.2196/17821] [Medline: 32543445]
Zeng K, Pan Z, Xu Y, Qu Y. An ensemble learning strategy for eligibility criteria text classification for clinical trial
recruitment: algorithm development and validation. JMIR Med Inform 2020 Jul 01;8(7):e17832 [FREE Full text] [doi:10.2196/17832] [Medline: 32609092]
Zhao Z, Yang M, Tang B, Zhao T. Re-examination of rule-based methods in deidentification of electronic health records:
algorithm development and validation. JMIR Med Inform 2020 Apr 30;8(4):e17622 [FREE Full text] [doi: 10.2196/17622][Medline: 32352384]
Zhang H, Ni W, Li J, Zhang J. Artificial intelligence-based traditional Chinese medicine assistive diagnostic system:
validation study. JMIR Med Inform 2020 Jun 15;8(6):e17608 [FREE Full text] [doi: 10.2196/17608] [Medline: 32538797]

Abbreviations
BERT: Bidirectional Encoder Representation from Transformers
NLP: Natural Language Processing
OHNLP: Open Health Natural Language Processing
TCM: Traditional Chinese Medicine

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Hao et al

Edited by C Lovis; submitted 27.08.20; peer-reviewed by M Cai, J Ainsworth; comments to author 11.10.20; revised version received
28.02.21; accepted 27.04.21; published 21.10.21
Please cite as:
Hao T, Huang Z, Liang L, Weng H, Tang B
Health Natural Language Processing: Methodology Development and Applications
JMIR Med Inform 2021;9(10):e23898
URL: https://medinform.jmir.org/2021/10/e23898
doi: 10.2196/23898
PMID:©Tianyong Hao, Zhengxing Huang, Likeng Liang, Heng Weng, Buzhou Tang. Originally published in JMIR Medical Informatics(https://medinform.jmir.org), 21.10.2021. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete
bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license
information must be included.

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