Evidence map›Paper›PMID 37367736›Full record

ArticleMedical sciences (Basel, Switzerland)2023

Patient Dietary Supplements Use: Do Results from Natural Language Processing of Clinical Notes Agree with Survey Data?

Douglas Redd, Terri Elizabeth Workman, Yijun Shao, Yan Cheng, Senait Tekle, Jennifer H Garvin, Cynthia A Brandt, Qing Zeng-Treitler

Open access · goldAbstract read
In one paragraph

Article in Medical sciences (Basel, Switzerland), 2023. 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
0.5field-weighted citation impact, top 36% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. 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 at 3 institutions in 1 country.

Douglas ReddCenter for Data Science and Outcome Research, Washington DC VA Medical Center, Washington, DC 20422, USA.
Terri Elizabeth WorkmanDepartment of Clinical Research and Leadership, George Washington University, Washington, DC 20037, USA.
Yijun ShaoDepartment of Clinical Research and Leadership, George Washington University, Washington, DC 20037, USA.
Yan ChengDepartment of Clinical Research and Leadership, George Washington University, Washington, DC 20037, USA.ORCID 0000-0001-7189-3991
Senait TekleDepartment of Clinical Research and Leadership, George Washington University, Washington, DC 20037, USA.
Jennifer H GarvinVA Salt Lake City Health Care System, Salt Lake City, UT 84148, USA.ORCID 0000-0002-8986-7211
Cynthia A BrandtVA Connecticut Healthcare System, West Haven, CT 06516, USA.
Qing Zeng-TreitlerCenter for Data Science and Outcome Research, Washington DC VA Medical Center, Washington, DC 20422, USA.ORCID 0000-0002-8353-7473
George Washington University · USUniversity of Utah · USYale University · US

Funding

Clinical and Translational Science Institute at Childrens NationalUL1TR001876 · NCATS · CHILDREN'S RESEARCH INSTITUTE · PI BELL, MICHAEL J · 2016 to 2021
$25.6M
Clinical and Translational Science Institute at Childrens National-Mentored Career Development CoreKL2TR001877 · NCATS · CHILDREN'S RESEARCH INSTITUTE · PI LUBAN, NAOMI L · 2016 to 2020
$2.0M
NCATS NIH HHS KL2 TR001877NCATS NIH HHS UL1 TR001876
6 · The paper itself

Abstract

There is widespread use of dietary supplements, some prescribed but many taken without a physician's guidance. There are many potential interactions between supplements and both over-the-counter and prescription medications in ways that are unknown to patients. Structured medical records do not adequately document supplement use; however, unstructured clinical notes often contain extra information on supplements. We studied a group of 377 patients from three healthcare facilities and developed a natural language processing (NLP) tool to detect supplement use. Using surveys of these patients, we investigated the correlation between self-reported supplement use and NLP extractions from the clinical notes. Our model achieved an F1 score of 0.914 for detecting all supplements. Individual supplement detection had a variable correlation with survey responses, ranging from an F1 of 0.83 for calcium to an F1 of 0.39 for folic acid. Our study demonstrated good NLP performance while also finding that self-reported supplement use is not always consistent with the documented use in clinical records.

Indexed as

Electronic Health RecordsNatural Language ProcessingDietary SupplementsHumansSelf Reportdietary supplementsmachine learningnatural language processing

Identifiers

PMID37367736
PMCPMC10304046
OpenAlexW4378082549

What OpenQuestion 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 OpenQuestion graph.