From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities

Our results indicate that data-driven algorithms can extract tumor progression outcomes from existing EHR data. However, ML methods are subject to varying availability bias, supporting contextual information, and pre-processing resource burdens that influence the extracted PFS endpoint distributions. Our scan-based CV results also suggest that the automation of clinical criteria may not align with human intuition. Our findings indicate a need for improved data source integration, validation, and revisiting of clinical criteria in parallel to multi-modal ML algorithm development.

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Publication Details

Journal
PLOS Digital Health
Published
2025-05-14
DOI
https://doi.org/10.1371/journal.pdig.0000755
Citations
2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
2.72
Controls
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article

From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities

Barış Türkbey, Sarisha Jagasia, Kevin Camphausen, Andra Krauze et al.
2 citations
PLOS Digital Health
Radiomics and Machine Learning in Medical Imaging
2.72
article

From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities

Barış Türkbey, Sarisha Jagasia, Kevin Camphausen, Andra Krauze, Stephanie A. Harmon, Shreya Chappidi, Erdal Taşçı, Jatinder Singh, Ying Zhuge, Mason J. Belue
article en
2 citations

Abstract

Our results indicate that data-driven algorithms can extract tumor progression outcomes from existing EHR data. However, ML methods are subject to varying availability bias, supporting contextual information, and pre-processing resource burdens that influence the extracted PFS endpoint distributions. Our scan-based CV results also suggest that the automation of clinical criteria may not align with human intuition. Our findings indicate a need for improved data source integration, validation, and revisiting of clinical criteria in parallel to multi-modal ML algorithm development.

PLOS Digital HealthVol. 4(5)
National Institutes of Health (US), University of Cambridge (GB), National Cancer Institute (US), Center for Cancer Research (US), University of Duisburg-Essen (DE)
Openalex Percentile: Top 11%
Radiomics and Machine Learning in Medical Imaging
2.72
DOIOpen Access PDFOpenAlex
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From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities — Barış Türkbey, Sarisha Jagasia, et al. · PLOS Digital Health (2025) | TGRS Research Map | TGRS