Confident interpretation of Bayesian decision tree ensembles for clinical applications.
IEEE Trans Inf Technol Biomed
; 11(3): 312-9, 2007 May.
Article
em En
| MEDLINE
| ID: mdl-17521081
ABSTRACT
Bayesian averaging (BA) over ensembles of decision models allows evaluation of the uncertainty of decisions that is of crucial importance for safety-critical applications such as medical diagnostics. The interpretability of the ensemble can also give useful information for experts responsible for making reliable decisions. For this reason, decision trees (DTs) are attractive decision models for experts. However, BA over such models makes an ensemble of DTs uninterpretable. In this paper, we present a new approach to probabilistic interpretation of Bayesian DT ensembles. This approach is based on the quantitative evaluation of uncertainty of the DTs, and allows experts to find a DT that provides a high predictive accuracy and confident outcomes. To make the BA over DTs feasible in our experiments, we use a Markov Chain Monte Carlo technique with a reversible jump extension. The results obtained from clinical data show that in terms of predictive accuracy, the proposed method outperforms the maximum a posteriori (MAP) method that has been suggested for interpretation of DT ensembles.
Buscar no Google
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Reconhecimento Automatizado de Padrão
/
Inteligência Artificial
/
Teorema de Bayes
/
Técnicas de Apoio para a Decisão
/
Diagnóstico por Computador
/
Sistemas de Apoio a Decisões Clínicas
Tipo de estudo:
Diagnostic_studies
/
Health_economic_evaluation
/
Prognostic_studies
Idioma:
En
Revista:
IEEE Trans Inf Technol Biomed
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2007
Tipo de documento:
Article
País de afiliação:
Reino Unido
País de publicação:
EEUU
/
ESTADOS UNIDOS
/
ESTADOS UNIDOS DA AMERICA
/
EUA
/
UNITED STATES
/
UNITED STATES OF AMERICA
/
US
/
USA