Evidence map›Paper›PMID 34673533›Full record

ArticleJMIR medical informatics2021

Health Natural Language Processing: Methodology Development and Applications.

Tianyong Hao, Zhengxing Huang, Likeng Liang, Heng Weng, Buzhou Tang

Abstract readEditorial
In one paragraph

Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed.

  1. Article
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  6. Large language models accurately identify immunosuppression in intensive care unit patients.Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Natural Language Processing andJMIR medical informatics · 2025
    Article
  11. Article
  12. Article
  13. Article
  14. Endometriosis Communities on Reddit: Quantitative Analysis.Journal of medical Internet research · 2025
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Tianyong HaoSchool of Computer Science, South China Normal University, Guangzhou, China.ORCID https://orcid.org/0000-0002-9792-3949
Zhengxing HuangCollege of Computer Science and Technology, Zhejiang University, Guangzhou, China.ORCID https://orcid.org/0000-0002-2644-8642
Likeng LiangSchool of Computer Science, South China Normal University, Guangzhou, China.ORCID https://orcid.org/0000-0003-0744-2955
Heng WengThe Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.ORCID https://orcid.org/0000-0002-5203-8663
Buzhou TangSchool of Computer Science and Technology, Harbin Institute of Technology Shenzhen, Shenzhen, China.ORCID https://orcid.org/0000-0003-0271-8246

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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".

Indexed as

applicationhealth caremethodologynatural language processingunstructured text

Identifiers

PMID34673533
PMCPMC8569540

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.