Evidence map›Paper›PMID 38102593›Full record

ArticleBMC bioinformatics2023

Machine learning-based donor permission extraction from informed consent documents.

Meng Zhang, Madhuri Sankaranarayanapillai, Jingcheng Du, Yang Xiang, Frank J Manion, Marcelline R Harris, Cooper Stansbury, Huy Anh Pham, Cui Tao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 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
–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. 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

9 authors.

Meng ZhangMcWilliam School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
Madhuri SankaranarayanapillaiMcWilliam School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
Jingcheng DuMcWilliam School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
Yang XiangMcWilliam School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
Frank J ManionSchool of Nursing, University of Michigan, Ann Arbor, MI, 48104, USA.
Marcelline R HarrisSchool of Nursing, University of Michigan, Ann Arbor, MI, 48104, USA.
Cooper StansburySchool of Nursing, University of Michigan, Ann Arbor, MI, 48104, USA.
Huy Anh PhamMcWilliam School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
Cui TaoMcWilliam School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. tao.cui@mayo.edu.ORCID http://orcid.org/0000-0002-4267-1924

Funding

Dynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4M
Metadata applications on informed content to facilitate biorepository data regulation and sharingU01HG009454 · NHGRI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI TAO, CUI · 2016 to 2018
$1.4M
SBIR Phase I- Topic 410 - Cancer Clinical Trials Recruitment and Retention Tools for Participant Engagement. 75N91020C00017 · NCI · MELAX TECHNOLOGIES, INC. · PI MANION, FRANK · 2020 to 2020
$400k
NCI NIH HHS 75N91020C00017NHGRI NIH HHS U01 HG009454NIAID NIH HHS R01 AI130460NIH HHS 75N91020C00017NIH HHS U01HG009454
6 · The paper itself

Abstract

backgroundWith more clinical trials are offering optional participation in the collection of bio-specimens for biobanking comes the increasing complexity of requirements of informed consent forms. The aim of this study is to develop an automatic natural language processing (NLP) tool to annotate informed consent documents to promote biorepository data regulation, sharing, and decision support. We collected informed consent documents from several publicly available sources, then manually annotated them, covering sentences containing permission information about the sharing of either bio-specimens or donor data, or conducting genetic research or future research using bio-specimens or donor data.

resultsWe evaluated a variety of machine learning algorithms including random forest (RF) and support vector machine (SVM) for the automatic identification of these sentences. 120 informed consent documents containing 29,204 sentences were annotated, of which 1250 sentences (4.28%) provide answers to a permission question. A support vector machine (SVM) model achieved a F-1 score of 0.95 on classifying the sentences when using a gold standard, which is a prefiltered corpus containing all relevant sentences.

conclusionsThis study provides the feasibility of using machine learning tools to classify permission-related sentences in informed consent documents.

Indexed as

Biological Specimen BanksConsent FormsAlgorithmsMachine LearningNatural Language ProcessingInformed consentMachine learningNatural language processingText classification

Identifiers

PMID38102593
PMCPMC10724888

What OpenQuestion holds

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LicenceCC BY
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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.