Evidence map›Paper›PMID 29888077›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2018

Inpatient Clinical Order Patterns Machine-Learned From Teaching Versus Attending-Only Medical Services.

Jason K Wang, Alejandro Schuler, Nigam H Shah, Michael T M Baiocchi, Jonathan H Chen

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Neural Networks for Clinical Order Decision Support.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019
    Article
  2. Article
  3. 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

5 authors.

Jason K WangMathematical & Computational Science Program, Stanford University, Stanford, CA, USA.
Alejandro SchulerCenter for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Nigam H ShahCenter for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Michael T M BaiocchiPrevention Research Center, Stanford University, Stanford, CA, USA.
Jonathan H ChenDepartment of Medicine, Stanford University, Stanford, CA, USA.

Funding

Cancer Systems Biology Scholars ProgramR25CA180993 · NCI · STANFORD UNIVERSITY · PI PLEVRITIS, SYLVIA KATINA · 2014 to 2018
$2.2M
Data-Mining Clinical Decision Support from Electronic Health RecordsK01ES026837 · NIEHS · STANFORD UNIVERSITY · PI CHEN, JONATHAN H. · 2015 to 2019
$893k
NCI NIH HHS R25 CA180993NIEHS NIH HHS K01 ES026837
6 · The paper itself

Abstract

Clinical order patterns derived from data-mining electronic health records can be a valuable source of decision support content. However, the quality of crowdsourcing such patterns may be suspect depending on the population learned from. For example, it is unclear whether learning inpatient practice patterns from a university teaching service, characterized by physician-trainee teams with an emphasis on medical education, will be of variable quality versus an attending-only medical service that focuses strictly on clinical care. Machine learning clinical order patterns by association rule episode mining from teaching versus attending-only inpatient medical services illustrated some practice variability, but converged towards similar top results in either case. We further validated the automatically generated content by confirming alignment with external reference standards extracted from clinical practice guidelines.

Identifiers

PMID29888077
PMCPMC5961816

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.