Evidence map›Paper›PMID 36038064›Full record

ArticleJournal of biomedical informatics2022

Unsupervised probabilistic models for sequential Electronic Health Records.

Alan D Kaplan, John D Greene, Vincent X Liu, Priyadip Ray

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Alan D KaplanComputational Engineering Division, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550, United States of America. Electronic address: kaplan7@llnl.gov.
John D GreeneKaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612, United States of America.
Vincent X LiuKaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612, United States of America.
Priyadip RayComputational Engineering Division, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550, United States of America.

Funding

Identifying pre-sepsis opportunities for early, targeted intervention - Equipment SupplementR35GM128672 · NIGMS · KAISER FOUNDATION RESEARCH INSTITUTE · PI Vincent Liu · 2018 to 2026
$4.3M
NIGMS NIH HHS R35 GM128672
6 · The paper itself

Abstract

We develop an unsupervised probabilistic model for heterogeneous Electronic Health Record (EHR) data. Utilizing a mixture model formulation, our approach directly models sequences of arbitrary length, such as medications and laboratory results. This allows for subgrouping and incorporation of the dynamics underlying heterogeneous data types. The model consists of a layered set of latent variables that encode underlying structure in the data. These variables represent subject subgroups at the top layer, and unobserved states for sequences in the second layer. We train this model on episodic data from subjects receiving medical care in the Kaiser Permanente Northern California integrated healthcare delivery system. The resulting properties of the trained model generate novel insight from these complex and multifaceted data. In addition, we show how the model can be used to analyze sequences that contribute to assessment of mortality likelihood.

Indexed as

Delivery of Health Care, IntegratedElectronic Health RecordsHumansModels, StatisticalProbabilityEHR dataMixture modelingSubgroup analysisUnsupervised learning

Identifiers

PMID36038064
PMCPMC10588733

What OpenQuestion holds

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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.