Evidence map›Paper›PMID 38189663›Full record

ArticleAlcohol, clinical & experimental research2024

Automated-detection of risky alcohol use prior to surgery using natural language processing.

V G Vinod Vydiswaran, Asher Strayhorn, Katherine Weber, Haley Stevens, Jessica Mellinger, G Scott Winder, Anne C Fernandez

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Alcohol, clinical & experimental research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
6.0field-weighted citation impact, top 4% of its field
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

5 citing papers in PubMed, 11 citations in OpenAlex.

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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

7 authors at 1 institution in 1 country.

V G Vinod VydiswaranDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.
Asher StrayhornDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.
Katherine WeberDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.
Haley StevensDepartment of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA.
Jessica MellingerDepartment of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA.
G Scott WinderDepartment of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA.
Anne C FernandezDepartment of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0002-9129-7318
University of Michigan · US

Funding

Leveraging electronic health records to identify risky alcohol use prior to surgeryR33AA028315 · NIAAA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI FERNANDEZ, ANNE CHRISTIE, VYDISWARAN, VG VINOD · 2022 to 2024
$1.1M
Improving Outcomes in Alcoholic Liver Disease: Integrating Behavioral Interventions into the Hepatology ClinicK23AA026333 · NIAAA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MELLINGER, JESSICA LEIGH · 2018 to 2022
$972k
Leveraging electronic health records to identify risky alcohol use prior to surgeryR21AA028315 · NIAAA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI FERNANDEZ, ANNE CHRISTIE, VYDISWARAN, VG VINOD · 2020 to 2021
$398k
NIAAA NIH HHS K23AA026333NIAAA NIH HHS R21 AA028315NIAAA NIH HHS R33 AA028315NIAAA NIH HHS R33AA028315
6 · The paper itself

Abstract

backgroundPreoperative risky alcohol use is one of the most common surgical risk factors. Accurate and early identification of risky alcohol use could enhance surgical safety. Artificial Intelligence-based approaches, such as natural language processing (NLP), provide an innovative method to identify alcohol-related risks from patients' electronic health records (EHR) before surgery.

methodsClinical notes (n = 53,629) from pre-operative patients in a tertiary care facility were analyzed for evidence of risky alcohol use and alcohol use disorder. One hundred of these records were reviewed by experts and labeled for comparison. A rule-based NLP model was built, and we assessed the clinical notes for the entire population. Additionally, we assessed each record for the presence or absence of alcohol-related International Classification of Diseases (ICD) diagnosis codes as an additional comparator.

resultsNLP correctly identified 87% of the human-labeled patients classified with risky alcohol use. In contrast, diagnosis codes alone correctly identified only 29% of these patients. In terms of specificity, NLP correctly identified 84% of the non-risky cohort, while diagnosis codes correctly identified 90% of this cohort. In the analysis of the full dataset, the NLP-based approach identified three times more patients with risky alcohol use than ICD codes.

conclusionsNLP, an artificial intelligence-based approach, efficiently and accurately identifies alcohol-related risk in patients' EHRs. This approach could supplement other alcohol screening tools to identify patients in need of intervention, treatment, and/or postoperative withdrawal prophylaxis. Alcohol-related ICD diagnosis had limited utility relative to NLP, which extracts richer information within clinical notes to classify patients.

Indexed as

alcohol drinkingalcohol use disorderartificial intelligencenatural language processingsurgery

Identifiers

PMID38189663
PMCPMC10783530
OpenAlexW4390673162

What OpenQuestion holds

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Registered trials

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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.