Evidence map›Paper›PMID 42277300›Full record

ArticleNPJ digital medicine2026

Embeddings of clinical codes enable knowledge-grounded AI in medicine.

Ruth Johnson, Uri Gottlieb, Galit Shaham, Lihi Eisen, Jacob Waxman, Stav Devons-Sberro, Curtis R Ginder, Peter Hong, Raheel Sayeed, Xiaorui Su and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Ruth JohnsonThe Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA.
Uri GottliebClalit Research Institute, Innovation Division, Clalit Health Services, Ramat-Gan, Israel.
Galit ShahamClalit Research Institute, Innovation Division, Clalit Health Services, Ramat-Gan, Israel.
Lihi EisenClalit Research Institute, Innovation Division, Clalit Health Services, Ramat-Gan, Israel.
Jacob WaxmanClalit Research Institute, Innovation Division, Clalit Health Services, Ramat-Gan, Israel.
Stav Devons-SberroClalit Research Institute, Innovation Division, Clalit Health Services, Ramat-Gan, Israel.
Curtis R GinderCardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Peter HongDivision of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA.
Raheel SayeedDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Xiaorui SuDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Ben Y ReisThe Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA.
Ran D BalicerThe Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA.
Noa DaganThe Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA. noada@clalit.org.il.
Marinka ZitnikThe Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA. marinka@hms.harvard.edu.

Funding

Measuring Neonatal RegionalizationR01HD108794 · NICHD · STANFORD UNIVERSITY · PI Jochen Profit, JEANNETTE A ROGOWSKI · 2023 to 2026
$2.8M
National Science Foundation, United States CAREER 2339524NICHD NIH HHS R01 HD108794NIH HHS R01-HD108794U.S. Department of Defense FA8702-15-D-0001
6 · The paper itself

Abstract

Standardization of electronic health records (EHRs) has enabled the use of clinical codes in AI. We introduce ClinVec, an embedding store that provides embeddings for 153,166 clinical codes and concepts across eight vocabularies. ClinVec embeds ClinGraph, a knowledge graph with over 2 million edges tailored to clinical vocabularies used in EHRs. We validate the embeddings using an inter-institutional clinician panel and N = 3767 clinical term pairs spanning 11 disease areas, and we find that embedding similarity reflects clinical relatedness. We use ClinVec for knowledge injection in large language model medical question answering and for unsupervised patient stratification and risk prediction. By providing a shared representation of clinical concepts, ClinVec supports knowledge-grounded AI systems for modeling patients and populations.

Identifiers

PMID42277300
PMCPMC13658233

What OpenQuestion holds

Textmetadata
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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.