Evidence map›Paper›PMID 25954443›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2014

Automated extraction of family history information from clinical notes.

Robert Bill, Serguei Pakhomov, Elizabeth S Chen, Tamara J Winden, Elizabeth W Carter, Genevieve B Melton

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 2 pooled it
–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

24 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Zero-shot Learning with Minimum Instruction to Extract Social Determinants and Family History from Clinical Notes using GPT Model.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2023
    Article
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  16. Defining Phenotypes from Clinical Data to Drive Genomic Research.Annual review of biomedical data science · 2018
    Article
  17. Generalized Extraction and Classification of Span-Level Clinical Phrases.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018
    Article
  18. Article
  19. Systematic Analysis of Free-Text Family History in Electronic Health Record.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017
    Article
  20. Identifying Family History and Substance Use Associations for Adult Epilepsy from the Electronic Health Record.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016
    Article
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

6 authors.

Robert BillInstitute for Health Informatics, University of Minnesota, Minneapolis, MN.
Serguei PakhomovInstitute for Health Informatics, University of Minnesota, Minneapolis, MN ; College of Pharmacy, University of Minnesota, Minneapolis, MN.
Elizabeth S ChenCenter for Clinical & Translational Science, University of Vermont, Burlington, VT ; Department of Medicine, University of Vermont, Burlington, VT.
Tamara J WindenInstitute for Health Informatics, University of Minnesota, Minneapolis, MN ; Division of Applied Research, Allina Health, Minneapolis, MN.
Elizabeth W CarterCenter for Clinical & Translational Science, University of Vermont, Burlington, VT.
Genevieve B MeltonInstitute for Health Informatics, University of Minnesota, Minneapolis, MN ; Department of Surgery, University of Minnesota, Minneapolis, MN.

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR000114 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R · 2012 to 2015
$35.0M
Natural language processing for clinical and translational researchR01GM102282 · NIGMS · MAYO CLINIC ROCHESTER · PI LIU, HONGFANG, PAKHOMOV, SERGUEI VS · 2013 to 2016
$2.5M
Leveraging the EHR to Collect and Analyze Social, Behavioral & Familial FactorsR01LM011364 · NLM · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI CHEN, ELIZABETH S., MELTON-MEAUX, GENEVIEVE B · 2012 to 2015
$1.4M
NCATS NIH HHS 8UL1TR000114-02NCATS NIH HHS UL1 TR000114NCRR NIH HHS U54 RR026066-01A2NIGMS NIH HHS 1 R01 GM102282-01A1NIGMS NIH HHS R01 GM102282NLM NIH HHS 1 R01 LM011364-01NLM NIH HHS R01 LM011364
6 · The paper itself

Abstract

Despite increased functionality for obtaining family history in a structured format within electronic health record systems, clinical notes often still contain this information. We developed and evaluated an Unstructured Information Management Application (UIMA)-based natural language processing (NLP) module for automated extraction of family history information with functionality for identifying statements, observations (e.g., disease or procedure), relative or side of family with attributes (i.e., vital status, age of diagnosis, certainty, and negation), and predication ("indicator phrases"), the latter of which was used to establish relationships between observations and family member. The family history NLP system demonstrated F-scores of 66.9, 92.4, 82.9, 57.3, 97.7, and 61.9 for detection of family history statements, family member identification, observation identification, negation identification, vital status, and overall extraction of the predications between family members and observations, respectively. While the system performed well for detection of family history statements and predication constituents, further work is needed to improve extraction of certainty and temporal modifications.

Indexed as

Electronic Health RecordsFamily HealthNatural Language ProcessingHumansInformation Storage and RetrievalMedical History Taking

Identifiers

PMID25954443
PMCPMC4419952

What OpenQuestion holds

Textmetadata
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.