Development of a machine learning modelto estimate length of stay in coronaryartery bypass grafting
The predictive model can be used to generate length of hospital stay indices that could be used as markers of efficiency and identify patients with the potential for prolonged hospitalization, helping the institution in managing beds, scheduling surgeries, and allocating resources.
Authors
- Carolina Seara Couto (ORCID: https://orcid.org/0000-0002-0373-3248)
- Tânia Moreira Grillo Pedrosa (ORCID: https://orcid.org/0000-0002-0042-8125)
- Luciana Moreira Seara (ORCID: https://orcid.org/0000-0001-9822-6056)
- Vitor Seara Couto (ORCID: https://orcid.org/0000-0001-5218-2158)
- Renato Camargos Couto (ORCID: https://orcid.org/0000-0003-4907-6295)
Institutions
- Universidade do Estado de Minas Gerais (BR)
- Centro Universitário de Belo Horizonte (BR)
- João Pinheiro Foundation (BR)
- Faculdade de Ciências Médicas de Minas Gerais (BR)
- Ministério da Saúde (ST)
Publication Details
- Journal
- Revista de Saúde Pública
- Published
- 2024-09-04
- DOI
- https://doi.org/10.11606/s1518-8787.2024058006161
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.42
Funders
- China Scholarship Council