Evidence map›Paper›PMID 28815117›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2017

Systematic Analysis of Free-Text Family History in Electronic Health Record.

Yanshan Wang, Liwei Wang, Majid Rastegar-Mojarad, Sijia Liu, Feichen Shen, Hongfang Liu

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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  6. Clinical concept extraction using transformers.Journal of the American Medical Informatics Association : JAMIA · 2020
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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

6 authors.

Yanshan WangDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Liwei WangDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Majid Rastegar-MojaradDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Sijia LiuDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Feichen ShenDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Hongfang LiuDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Family history is an important component in modern clinical care especially in the era of precision medicine. Family history information in the Electronic Health Record (EHR) system is usually stored in structured format as well as in free-text format. In this study, we systematically analyzed a family history text corpus from 3 million clinical notes for the patients receiving their primary care at Mayo Clinic. Family members, medical problems, and their associations were analyzed and reported. Our findings showed a great agreement between positive/negated medical problems mentioned in the diagnosis report and the family history, as measured by observed agreement and random agreement. We also found that the family history of some medical problems existed up to 10~15 years prior to the diagnosis date of such problems. Finally two patient cases were studied to show the medical problems in the diagnosis and family history associated with the timeline.

Identifiers

PMID28815117
PMCPMC5543380

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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.