Comparison of Explainable Machine-Learning Models for Decision-Making in Health Intensive Care Using SHapley Additive exPlanations

Author:

Vidal Igor Pereira1ORCID,Pereira Marluce Rodrigues2ORCID,Freire André Pimenta1ORCID,Resende Uanderson3ORCID,Maziero Erick Galani2ORCID

Affiliation:

1. Departamento de Ciência da Computação, Universidade Federal de Lavras, Brazil

2. Departamento de Computação Aplicada, Universidade Federal de Lavras, Brazil

3. Hospital Santa Lúcia Poços de Caldas, Brazil

Funder

Fundação de Amparo à Pesquisa do Estado de Minas Gerais

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

ACM

Reference18 articles.

1. Arun Das and Paul Rad . 2020. Opportunities and challenges in explainable artificial intelligence (xai): A survey. arXiv preprint arXiv:2006.11371 ( 2020 ). Arun Das and Paul Rad. 2020. Opportunities and challenges in explainable artificial intelligence (xai): A survey. arXiv preprint arXiv:2006.11371 (2020).

2. Hong-Fei Deng , Ming-Wei Sun , Yu Wang , Jun Zeng , Ting Yuan , Ting Li , Di-Huan Li , Wei Chen , Ping Zhou , Qi Wang , 2021. Evaluating machine learning models for sepsis prediction: A systematic review of methodologies. Iscience ( 2021 ), 103651. Hong-Fei Deng, Ming-Wei Sun, Yu Wang, Jun Zeng, Ting Yuan, Ting Li, Di-Huan Li, Wei Chen, Ping Zhou, Qi Wang, 2021. Evaluating machine learning models for sepsis prediction: A systematic review of methodologies. Iscience (2021), 103651.

3. Diretrizes para tratamento da sepse grave/choque séptico: abordagem do agente infeccioso - diagnóstico

4. Principais bactérias causadoras de sepse: sepse em unidade de terapia intensiva

5. Pedro Celiny Ramos Garcia , Cristian Tedesco Tonial , and Jefferson Pedro Piva . 2020. Septic shock in pediatrics: the state-of-the-art. Jornal de pediatria 96 ( 2020 ), 87–98. Pedro Celiny Ramos Garcia, Cristian Tedesco Tonial, and Jefferson Pedro Piva. 2020. Septic shock in pediatrics: the state-of-the-art. Jornal de pediatria 96 (2020), 87–98.

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