Machine learning to predict 30-day quality-adjusted survival in critically ill patients with cancer.

Journal: Journal of critical care
PMID: 31715534

Abstract

PURPOSE: To develop and compare the predictive performance of machine-learning algorithms to estimate the risk of quality-adjusted life year (QALY) lower than or equal to 30 days (30-day QALY).

Authors

  • Hellen Geremias Dos Santos
    Faculdade de Saúde Pública, Universidade de São Paulo, São Paulo, Brasil.
  • Fernando Godinho Zampieri
    Research Institute, Heart Hospital (Hospital do Coração - Hcor), São Paulo, São Paulo, Brazil.
  • Karina Normilio-Silva
    Research Institute, Heart Hospital (Hospital do Coração - Hcor), São Paulo, São Paulo, Brazil; Cancer Institute of the State of São Paulo (Instituto do Câncer do Estado de São Paulo - ICESP), São Paulo, São Paulo, Brazil.
  • Gisela Tunes da Silva
    Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.
  • Antonio Carlos Pedroso de Lima
    Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.
  • Alexandre Biasi Cavalcanti
    Research Institute, Heart Hospital (Hospital do Coração - Hcor), São Paulo, São Paulo, Brazil; Cancer Institute of the State of São Paulo (Instituto do Câncer do Estado de São Paulo - ICESP), São Paulo, São Paulo, Brazil.
  • Alexandre Dias Porto Chiavegatto Filho
    From the Department of Epidemiology, School of Public Health of the University of Sao Paulo, Sao Paulo, SP, Brazil.

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