Beyond Random Splitting: Evaluating the Impact of Data Partitioning Strategies on Ventilator-Associated Pneumonia Prediction Using EHRs.

Miriam Asare-Baiden, Wenhui Zhang, Vicki Stover Hertzberg, Joyce C Ho

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Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Miriam Asare-BaidenComputer Science Department, Emory University, Atlanta, GA.

Wenhui ZhangCenter for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA.

Vicki Stover HertzbergCenter for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA.

Joyce C HoComputer Science Department, Emory University, Atlanta, GA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ventilator-Associated Pneumonia (VAP) significantly impacts critical care outcomes, yet prediction models often over-look healthcare data's meronomic structure. Using MIMIC-III data, we developed a multi-source extraction approach integrating structured data with clinical notes, identifying 679 VAP and 3,207 non-VAP cases. We compared four data splitting strategies: Ventilator Session-Based Split, Ventilator Session-Based Split on Single ICU Stays, Hospital Admission-Based Split, and Hospital Admission-Based Split on Single ICU Stays. Evaluating four machine learning models revealed that conventional random splitting yielded moderately high performance (AUROC: 76-81%) while restricting to single ICU stays surprisingly improved performance (AUROC: 86-87%). Admission-based approaches showed realistic results (AUROC: 72-76%). Feature analysis identified mechanical ventilation hours, systolic blood pressure, and urine counts as consistently important predictors. These findings demonstrate that robust VAP prediction requires evaluation frameworks respecting healthcare data's meronomic nature.

Indexed as

Electronic Health RecordsMachine LearningPneumonia, Ventilator-AssociatedHumansIntensive Care UnitsPredictive Learning Models

Identifiers

PMID41726395

PMCPMC12919537

What OpenQuestion holds

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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.