Work

Beyond the Black-Box: Understanding XGBoost to Predict Pediatric Hospital Readmission

Year: 2025

Type: conference-paper

Study design: Observational Study

Source: IEEE International Symposium on Computer-Based Medical Systems (CBMS)

DOI: 10.1109/cbms65348.2025.00145

Authors: Nayara Cristina Da Silva, Marcelo Keese Albertini, André Ricardo Backes, Geórgia Das Graças Pena

Institutions: Universidade Federal de Uberlândia, Universidade Federal de São Carlos

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: closed

Funders: Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de Minas Gerais

Awards: G2128734333, ESTUDO DE TÉCNICAS DE INTELIGÊNCIA ARTIFICIAL APLICADAS A ÁREA DA SAÚDE PARA MINIMIZAR DESFECHOS HOSPITALARES NEGATIVOS

OpenAlex ID: W4412030956

FWCI:

Cites: 27

Cited by:

Related to: 10