Learning Bayesian networks from survival data using weighting censored instances (CROSBI ID 160081)
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Podaci o odgovornosti
Štajduhar, Ivan ; Dalbelo Bašić, Bojana
engleski
Learning Bayesian networks from survival data using weighting censored instances
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.
Bayesian network; prognostic model; survival analysis; weighting censored instances; medical decision support
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Podaci o izdanju
43 (4)
2010.
613-622
objavljeno
1532-0464
10.1016/j.jbi.2010.03.005