Evidence map›Paper›PMID 40615692›Full record

ArticleNPJ digital medicine2025

Label efficient phenotyping for Long COVID using electronic health records.

Chuan Hong, Jun Wen, Harrison G Zhang, Vidul Ayakulangara Panickan, Doris Y Yang, Alicia W Chen, Xin Xiong, Xuan Wang, Michele Morris, Sara Morini and 15 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

25 authors.

Chuan Hong *Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
Jun Wen *Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0001-5067-2647
Harrison G Zhang *Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0001-6679-1464
Vidul Ayakulangara PanickanDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0003-0616-0403
Doris Y YangDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-5188-2571
Alicia W ChenDivision of Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA.
Xin XiongDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-1162-5220
Xuan WangDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Michele MorrisDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Sara MoriniDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Rahul SangarDivision of Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA.
Andrew DeyDivision of Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA.
Malarkodi J SamayamuthuDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Katherine LiaoDivision of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA, USA.
Clara-Lea BonzelDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Vidisha TanukondaVA Atlanta Healthcare System, Decatur, GA, USA.
Monika MaripuriDivision of Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA.
Jacqueline HonerlawDivision of Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA.
Yuk-Lam HoDivision of Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA.ORCID http://orcid.org/0000-0003-3305-3830
Shyam VisweswaranDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.ORCID http://orcid.org/0000-0002-2079-8684
Isaac S KohaneDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Kelly ChoDivision of Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA.
Gabriel Brat *Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0003-3928-5931
Zongqi Xia *Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Tianxi Cai *Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. tcai@hsph.harvard.edu.ORCID http://orcid.org/0000-0002-5379-2502

Funding

Leveraging electronic health records to optimize treatment selection and response in multiple sclerosisR01NS098023 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Zongqi Xia · 2016 to 2026
$4.6M
Real-world impact of the COVID-19 pandemic in people with multiple sclerosisR01NS124882 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI XIA, ZONGQI · 2022 to 2024
$1.2M
NINDS NIH HHS R01 NS098023NINDS NIH HHS R01 NS124882
6 · The paper itself

Abstract

Long COVID poses a significant disease burden globally, but its heterogeneous presentation and unreliable coding practices render it difficult to study. Developing efficient phenotyping algorithms is crucial to enabling risk prediction and effective management of Long COVID. We introduce the LAbel-efficienT Long COVID pHenotyping (LATCH) algorithm, which synthesizes a small number of gold-standard labels and a large, unlabeled dataset with many electronic health record (EHR) features. Both internal validation and external validation demonstrated the superior performance of LATCH over methods using the U09.9 Long COVID EHR code alone. Our downstream analysis revealed a pattern of elevated healthcare utilization due to Long COVID, peaking at and continuing beyond the fourth month following COVID infection. LATCH enhances the classification of Long COVID by fully utilizing both labeled and unlabeled data, providing vital insights into healthcare utilization trends, informing clinical and public health responses to the enduring consequences of COVID-19.

Identifiers

PMID40615692
PMCPMC12227522

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.