J Biomed Inform - Learning Bayesian networks from survival data using weighting censored instances.

Tópicos

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Resumo

Different survival data pre-processing procedures and adaptations of existing machine-learning techniques have been successfully applied to numerous fields in clinical medicine. Zupan et al. (2000) proposed handling censored survival data by assigning distributions of outcomes to shortly observed censored instances. In this paper, we applied their learning technique to two well-known procedures for learning Bayesian networks: a search-and-score hill-climbing algorithm and a constraint-based conditional independence algorithm. The method was thoroughly tested in a simulation study and on the publicly available clinical dataset GBSG2. We compared it to learning Bayesian networks by treating censored instances as event-free and to Cox regression. The results on model performance suggest that the weighting approach performs best when dealing with intermediate censoring. There is no significant difference between the model structures learnt using either the weighting approach or by treating censored instances as event-free, regardless of censoring.

Resumo Limpo

differ surviv data preprocess procedur adapt exist machinelearn techniqu success appli numer field clinic medicin zupan et al propos handl censor surviv data assign distribut outcom short observ censor instanc paper appli learn techniqu two wellknown procedur learn bayesian network searchandscor hillclimb algorithm constraintbas condit independ algorithm method thorough test simul studi public avail clinic dataset gbsg compar learn bayesian network treat censor instanc eventfre cox regress result model perform suggest weight approach perform best deal intermedi censor signific differ model structur learnt use either weight approach treat censor instanc eventfre regardless censor

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