Evidence map›Paper›PMID 33712852›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2021

Electronic phenotyping of health outcomes of interest using a linked claims-electronic health record database: Findings from a machine learning pilot project.

Teresa B Gibson, Michael D Nguyen, Timothy Burrell, Frank Yoon, Jenna Wong, Sai Dharmarajan, Rita Ouellet-Hellstrom, Wei Hua, Yong Ma, Elande Baro and 5 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.8field-weighted citation impact, top 6% of its field
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

11 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023
    Article
  7. The US Food and Drug Administration Sentinel System: a national resource for a learning health system.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  8. Article
  9. Article
  10. Classifying Infection Risk Following Pediatric Cardiac Surgery.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022
    Article
  11. 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

15 authors at 3 institutions in 1 country.

Teresa B GibsonGovernment Health and Human Services, IBM Watson Health, Bethesda, Maryland, USA.ORCID 0000-0002-5853-7447
Michael D NguyenFood and Drug Administration, Silver Spring, Maryland, USA.
Timothy BurrellGovernment Health and Human Services, IBM Watson Health, Bethesda, Maryland, USA.
Frank YoonGovernment Health and Human Services, IBM Watson Health, Bethesda, Maryland, USA.
Jenna WongHarvard Medical School and Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts, USA.
Sai DharmarajanOffice of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA.
Rita Ouellet-HellstromDivision of Epidemiology II, Office of Pharmacovigilance and Epidemiology, Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA.
Wei HuaFood and Drug Administration, Silver Spring, Maryland, USA.
Yong MaCenter for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA.
Elande BaroDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
Sarah BloemersGovernment Health and Human Services, IBM Watson Health, Bethesda, Maryland, USA.
Cory PackGovernment Health and Human Services, IBM Watson Health, Bethesda, Maryland, USA.
Adee KennedyHarvard Medical School and Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts, USA.
Sengwee TohHarvard Medical School and Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts, USA.
Robert BallOffice of Surveillance and Epidemiology Center for Drug Evaluation and Research U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
Harvard University · USCenter for Drug Evaluation and Research · USUnited States Food and Drug Administration · US

Funding

FDA HHS HHSF223201400030I
6 · The paper itself

Abstract

objectiveClaims-based algorithms are used in the Food and Drug Administration Sentinel Active Risk Identification and Analysis System to identify occurrences of health outcomes of interest (HOIs) for medical product safety assessment. This project aimed to apply machine learning classification techniques to demonstrate the feasibility of developing a claims-based algorithm to predict an HOI in structured electronic health record (EHR) data. MATERIALS AND

methodsWe used the 2015-2019 IBM MarketScan Explorys Claims-EMR Data Set, linking administrative claims and EHR data at the patient level. We focused on a single HOI, rhabdomyolysis, defined by EHR laboratory test results. Using claims-based predictors, we applied machine learning techniques to predict the HOI: logistic regression, LASSO (least absolute shrinkage and selection operator), random forests, support vector machines, artificial neural nets, and an ensemble method (Super Learner).

resultsThe study cohort included 32 956 patients and 39 499 encounters. Model performance (positive predictive value [PPV], sensitivity, specificity, area under the receiver-operating characteristic curve) varied considerably across techniques. The area under the receiver-operating characteristic curve exceeded 0.80 in most model variations. DISCUSSION: For the main Food and Drug Administration use case of assessing risk of rhabdomyolysis after drug use, a model with a high PPV is typically preferred. The Super Learner ensemble model without adjustment for class imbalance achieved a PPV of 75.6%, substantially better than a previously used human expert-developed model (PPV = 44.0%).

conclusionsIt is feasible to use machine learning methods to predict an EHR-derived HOI with claims-based predictors. Modeling strategies can be adapted for intended uses, including surveillance, identification of cases for chart review, and outcomes research.

Indexed as

Electronic Health RecordsMachine LearningElectronicsHumansOutcome Assessment, Health CarePilot Projectsadministrative claimselectronic health recordselectronic phenotypinghealthcarerhabdomyolysissupervised machine learning

Identifiers

PMID33712852
PMCPMC8279790
OpenAlexW3135690330

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.