Interpretable risk models for Sleep Apnea and Coronary diseases from structured and non-structured data

Carlos Anderson Oliveira Silva, Rafael Gonzalez-Otero, Michel Bessani, Liliana Otero Mendoza, Cristiano L. de Castro

Producción: Contribución a una revistaArtículorevisión exhaustiva

1 Cita (Scopus)

Resumen

Machine learning-based risk models built from Electronic Health Records (EHR) can support medical decision-making. However, the lack of standardization of EHR data and the “black-box” nature of the machine learning approaches have imposed difficulties to their acceptance as support tools in a clinical environment. This paper presents a method able to predict and explain the diagnostic of Atrial Fibrillation (AF), Sleep Apnea (SA) and, Coronary Arterial Disease (CAD); the proposed model is learned using EHR's structured data (commonly used screening variables) and non-structured data (textual data drawn from medical reports) of patients. An embedding scheme of variables together with a labeling approach is used to mimic the ability of an expert in categorizing the non-structured textual data. The method relies on complex models to predict such diseases combined with the SHAP approach to explaining the prediction. A comparison of prediction models with different settings of input variables has shown that the use of non-structured data improved the performances of CAD risk prediction models. Moreover, such a comparison pointed out that the patients’ medical histories is an important factor that should be considered during the data-driven learning process.

Idioma originalInglés
Número de artículo116955
PublicaciónExpert Systems with Applications
Volumen200
DOI
EstadoPublicada - 15 ago. 2022

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