Evidence map›Paper›PMID 37778672›Full record

ArticleJournal of biomedical informatics2023

A deep learning approach for transgender and gender diverse patient identification in electronic health records.

Yining Hua, Liqin Wang, Vi Nguyen, Meghan Rieu-Werden, Alex McDowell, David W Bates, Dinah Foer, Li Zhou

Abstract read
PubMed Full text at PMC Publisher
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Article in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. A scoping review of models to identify transgender patients in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Development of a natural language processing algorithm to extract social determinants of health from clinician notes.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2025
    Article
  11. Article
  12. Article
  13. Article
  14. The Growing Impact of Natural Language Processing in Healthcare and Public Health.Inquiry : a journal of medical care organization, provision and financing
    Review
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

8 authors.

Yining HuaDivision of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard T.H Chan School of Public Health, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. Electronic address: yininghua@g.harvard.edu.
Liqin WangDivision of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. Electronic address: lwang@bwh.harvard.edu.
Vi NguyenDivision of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. Electronic address: vnguyen31@bwh.harvard.edu.
Meghan Rieu-WerdenDivision of General Medicine, Massachusetts General Hospital, Boston, MA, USA. Electronic address: mrieuwerden@mgh.harvard.edu.
Alex McDowellHealth Policy Research Institute, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA; Department of Health Care Policy, Harvard Medical School, Boston, MA, USA. Electronic address: amcdowell4@mgh.harvard.edu.
David W BatesDivision of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. Electronic address: dbates@bwh.harvard.edu.
Dinah FoerDivision of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Division of Allergy and Clinical Immunology, Department of Medicine, Brigham and Women's Hospital, USA. Electronic address: dfoer@bwh.harvard.edu.
Li ZhouDivision of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. Electronic address: lzhou@bwh.harvard.edu.

Funding

Clinical Informatics to Advance Epidemiology and Pharmacogenetics of Serious Cutaneous Adverse Drug ReactionsR01AI150295 · NIAID · BRIGHAM AND WOMEN'S HOSPITAL · PI ZHOU, LI · 2019 to 2022
$2.8M
Effects of GLP-1 Receptor Agonists on Airway Inflammation and Platelet Activation in AsthmaK23HL161332 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI Dinah Foer · 2022 to 2026
$975k
Policy effects on gender minority health and evidence from the implementation of gender affirming coverage in MedicaidK01HS028916 · AHRQ · MASSACHUSETTS GENERAL HOSPITAL · PI MCDOWELL, ALEXANDRA · 2022 to 2024
$467k
Antecedents and Outcomes of Subjective Cognitive Decline: An Electronic Health Records and Artificial Intelligence ApproachK99AG075190 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI WANG, LIQIN · 2022 to 2023
$217k
AHRQ HHS K01 HS028916NHLBI NIH HHS K23 HL161332NIAID NIH HHS R01 AI150295NIA NIH HHS K99 AG075190
6 · The paper itself

Abstract

backgroundAlthough accurate identification of gender identity in the electronic health record (EHR) is crucial for providing equitable health care, particularly for transgender and gender diverse (TGD) populations, it remains a challenging task due to incomplete gender information in structured EHR fields.

objectiveUsing TGD identification as a case study, this research uses NLP and deep learning to build an accurate patient gender identity predictive model, aiming to tackle the challenges of identifying relevant patient-level information from EHR data and reducing annotation work.

methodsThis study included adult patients in a large healthcare system in Boston, MA, between 4/1/2017 to 4/1/2022. To identify relevant information from massive clinical notes, we compiled a list of gender-related keywords through expert curation, literature review, and expansion via a fine-tuned BioWordVec model. This keyword list was used to pre-screen potential TGD individuals and create two datasets for model training, testing, and validation. Dataset I was a balanced dataset that contained clinician-confirmed TGD patients and cases without keywords. Dataset II contained cases with keywords. The performance of the deep learning model was compared to traditional machine learning and rule-based algorithms.

resultsThe final keyword list consists of 109 keywords, of which 58 (53.2%) were expanded by the BioWordVec model. Dataset I contained 3,150 patients (50% TGD) while Dataset II contained 200 patients (90% TGD). On Dataset I the deep learning model achieved a F1 score of 0.917, sensitivity of 0.854, and a precision of 0.980; and on Dataset II a F1 score of 0.969, sensitivity of 0.967, and precision of 0.972. The deep learning model significantly outperformed rule-based algorithms.

conclusionThis is the first study to show that deep learning-integrated NLP algorithms can accurately identify gender identity using EHR data. Future work should leverage and evaluate additional diverse data sources to generate more generalizable algorithms.

Indexed as

Deep LearningTransgender PersonsAdultAlgorithmsElectronic Health RecordsFemaleGender IdentityHumansMaleElectronic health recordsGender identityMachine learningNatural language processingSexual and gender minoritiesTransgender persons

Identifiers

PMID37778672
PMCPMC10687838

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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.