Evidence map›Paper›PMID 40672759›Full record

ArticleOpen forum infectious diseases2025

Sensitivity and Specificity of Natural Language Processing Systems for Identification of Hospitalized People Who Use Drugs.

Leah Benrubi, Taisuke Sato, Leo K Westgard, Kyle Zollo-Venecek, Brindet Socrates, Benjamin Sweigart, Jessica P Ridgway, Joji Suzuki, Yoelkys Morales, David Goodman-Meza and 1 more

Abstract read
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Article in Open forum infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Leah BenrubiSchool of Medicine, Tufts University, Boston, MA, USA.ORCID https://orcid.org/0000-0002-4179-1502
Taisuke SatoTufts Medicine, Boston, MA, USA.ORCID https://orcid.org/0000-0001-6830-2812
Leo K WestgardTufts Medicine, Boston, MA, USA.
Kyle Zollo-VenecekTufts Medicine, Boston, MA, USA.ORCID https://orcid.org/0000-0002-1615-590X
Brindet SocratesTufts Medicine, Boston, MA, USA.
Benjamin SweigartTufts Medicine, Boston, MA, USA.
Jessica P RidgwaySchool of Medicine, University of Chicago, Chicago, IL, USA.
Joji SuzukiBrigham and Women's Hospital, Boston, MA, USA.ORCID https://orcid.org/0000-0001-5659-6147
Yoelkys MoralesSchool of Medicine, Tufts University, Boston, MA, USA.
David Goodman-MezaKirby Institute, University of New South Wales, Sydney, Australia.ORCID https://orcid.org/0000-0001-9382-3564
Alysse G WurcelSchool of Medicine, Tufts University, Boston, MA, USA.ORCID https://orcid.org/0000-0002-8255-8387

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: People who use drugs (PWUD) often lack access to optimal harm reduction and substance use disorder treatment tools. Tracking the epidemiology of acute care utilization by PWUD is crucial to improving systems of care. Chart reviews and Methods: We analyzed admissions at Tufts Medical Center between 2018 and 2023. Two NLP tools (Regular Expression and Open Health NLP Toolkit) were developed to identify PWUD and were compared with Results: Conclusions: NLP is an innovative tool that can create functional, cost-effective, and accurate systems of identifying hospitalized PWUD. These findings support further development of NLP technologies to improve health care equity for PWUD.

Indexed as

harm reductioninfectious diseasesinjection drug usenatural language processingpeople who use drugs

Identifiers

PMID40672759
PMCPMC12264426

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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.