Interpretable machine learning for mortality modeling on patients with chronic diseases considering the COVID-19 pandemic in a region of Chile: A Shapley value based approach

Author:

Barría-Sandoval Claudia12ORCID,Ferreira Guillermo3,Espinoza Venegas Maritza2ORCID,Marchant Vicente3ORCID

Affiliation:

1. Facultad de Ciencias para el Cuidado de la Salud, Universidad San Sebastián, Sede Concepción, Chile

2. Facultad de Enfermería, Universidad de Concepción, Concepción, Chile

3. Departamento de Estadística, Facultad de Ciencias Físicas y Matemáticas, Universidad de Concepción, Concepción, Chile

Publisher

Informa UK Limited

Reference46 articles.

1. An easy-to-use nomogram for predicting in-hospital mortality risk in COVID-19: a retrospective cohort study in a university hospital

2. Improving palliative care with deep learning;Avati A;BMC Med Inform Decis Mak,2018

3. Batiste-Alentorn XG, Novellas JA, Martínez CL, Calsina-Berna À. 2017. Manual de atención integral de personas con enfermedades crónicas avanzadas: aspectos clínicos. Elsevier Health Sciences.

4. Breiman LJ, Charles F, Stone J, Olshen RA. 1994. Classification and regression trees. New York: Wadsworth.

5. How many people need palliative care for cancer and non-cancer diseases in a middle-income country? Analysis of mortality data;Calvache JA;Colombian J Anestesiol,2020

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