JAMIA open · 2026
Enhancing cause of death prediction: development and validation of machine learning models using multimodal data across multiple health-care sites.
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Al-Garadi Mohammed, Desai Rishi J, Ngan Kerry, LeNoue-Newton Michele, Reeves Ruth M, Park Daniel, Hernández-Muñoz Jose J, Wang Shirley V, Maro Judith C, Fuller Candace C, Kueiyu Joshua Lin, Kuzucan Aida, Coughlin Kevin, Pillai Haritha, McPheeters Melissa, Whitaker Jill, Buckner Jessica A, McLemore Michael F, Westerman Dax M, Matheny Michael E
Аннотация
OBJECTIVES: To develop and validate machine learning (ML) models that predict probable cause of death (CoD) using structured electronic health record (EHR) data, unstructured clinical notes, and publicly available sources.
MATERIALS AND METHODS: This multi-institutional retrospective study was conducted across Vanderbilt University Medical Center (VUMC) and Massachusetts General Brigham (MGB), including deceased patients with encounters between October 1, 2015, and January 1, 2021, and confirmed death records. The cohort included 13 708 patients from VUMC and 34 839 from MGB.The primary outcome was underlying CoD categorized into the top 15 National Center for Health Statistics rankable causes, with others grouped as "Other." Performance was assessed using weighted area under the receiver operating characteristic curve (AUC) and F-measure.
RESULTS: The XGBoost model using structured EHR data alone achieved weighted AUCs of 0.86 (95% CI, 0.84-0.88) at VUMC and 0.80 (95% CI, 0.79-0.80) at MGB. Adding unstructured notes improved performance, with weighted AUCs of 0.90 (95% CI, 0.88-0.93) at VUMC and 0.92 (95% CI, 0.91-0.92) at MGB. Adding publicly available data did not further improve performance. Cross-institutional validation revealed significant performance degradation.
DISCUSSION: Models integrating structured and unstructured EHR data show strong within-institution performance but limited generalizability across healthcare systems, highlighting challenges related to institutional data heterogeneity.
CONCLUSIONS: Machine learning models combining structured and unstructured EHR data accurately predict CoD within institutions but perform poorly across sites. Health-care institutions may benefit from adopting robust processes for locally tailored models, and future research should focus on enhancing model generalizability while addressing unique institutional data environments.
MATERIALS AND METHODS: This multi-institutional retrospective study was conducted across Vanderbilt University Medical Center (VUMC) and Massachusetts General Brigham (MGB), including deceased patients with encounters between October 1, 2015, and January 1, 2021, and confirmed death records. The cohort included 13 708 patients from VUMC and 34 839 from MGB.The primary outcome was underlying CoD categorized into the top 15 National Center for Health Statistics rankable causes, with others grouped as "Other." Performance was assessed using weighted area under the receiver operating characteristic curve (AUC) and F-measure.
RESULTS: The XGBoost model using structured EHR data alone achieved weighted AUCs of 0.86 (95% CI, 0.84-0.88) at VUMC and 0.80 (95% CI, 0.79-0.80) at MGB. Adding unstructured notes improved performance, with weighted AUCs of 0.90 (95% CI, 0.88-0.93) at VUMC and 0.92 (95% CI, 0.91-0.92) at MGB. Adding publicly available data did not further improve performance. Cross-institutional validation revealed significant performance degradation.
DISCUSSION: Models integrating structured and unstructured EHR data show strong within-institution performance but limited generalizability across healthcare systems, highlighting challenges related to institutional data heterogeneity.
CONCLUSIONS: Machine learning models combining structured and unstructured EHR data accurately predict CoD within institutions but perform poorly across sites. Health-care institutions may benefit from adopting robust processes for locally tailored models, and future research should focus on enhancing model generalizability while addressing unique institutional data environments.