Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay.

Khadem, Heydar; Nemat, Hoda; Elliott, Jackie; Benaissa, Mohammed

Khadem, Heydar; Nemat, Hoda; Elliott, Jackie; Benaissa, Mohammed.

Afiliación

Khadem H; Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UK.
Nemat H; Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UK.
Elliott J; Department of Oncology and Metabolism, University of Sheffield, Sheffield S10 2TN, UK.
Benaissa M; Teaching Hospitals, Diabetes and Endocrine Centre, Northern General Hospital, Sheffield S5 7AU, UK.

Sensors (Basel) ; 22(22)2022 Nov 12.

Article en En | MEDLINE | ID: mdl-36433354

Asunto(s)

COVID-19; Diabetes Mellitus; Humanos; Pacientes Internos; Aprendizaje Automático; Mortalidad Hospitalaria

Palabras clave

COVID-19; SHAP; diabetes mellitus; machine learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Diabetes Mellitus / COVID-19 Tipo de estudio: Etiology_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Suiza

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