Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help

Connections to large medical centers are not enough to promote accurate ML at all sites within a healthcare system. Data augmentation and SDG may provide the necessary data volumes to enable local recalibration at smaller facilities.

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Publication Details

Journal
Journal of Biomedical Informatics
Published
2026-03-11
DOI
https://doi.org/10.1016/j.jbi.2026.105012
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help

Sharon E. Davis, Bradley Malin, Katherine L Brown
Journal of Biomedical Informatics
Sepsis Diagnosis and Treatment
article

Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help

Sharon E. Davis, Bradley Malin, Katherine L Brown
article en

Abstract

Connections to large medical centers are not enough to promote accurate ML at all sites within a healthcare system. Data augmentation and SDG may provide the necessary data volumes to enable local recalibration at smaller facilities.

Journal of Biomedical InformaticsVol. 177
Vanderbilt University Medical Center (US)
Sustainable cities and communities
Openalex Percentile: Top 26%
Sepsis Diagnosis and Treatment
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Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help — Sharon E. Davis, Bradley Malin, et al. · Journal of Biomedical Informatics (2026) | TGRS Research Map | TGRS