J Biomed Inform - Learning patient-specific predictive models from clinical data.

Tópicos

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Resumo

We introduce an algorithm for learning patient-specific models from clinical data to predict outcomes. Patient-specific models are influenced by the particular history, symptoms, laboratory results, and other features of the patient case at hand, in contrast to the commonly used population-wide models that are constructed to perform well on average on all future cases. The patient-specific algorithm uses Markov blanket (MB) models, carries out Bayesian model averaging over a set of models to predict the outcome for the patient case at hand, and employs a patient-specific heuristic to locate a set of suitable models to average over. We evaluate the utility of using a local structure representation for the conditional probability distributions in the MB models that captures additional independence relations among the variables compared to the typically used representation that captures only the global structure among the variables. In addition, we compare the performance of Bayesian model averaging to that of model selection. The patient-specific algorithm and its variants were evaluated on two clinical datasets for two outcomes. Our results provide support that the performance of an algorithm for learning patient-specific models can be improved by using a local structure representation for MB models and by performing Bayesian model averaging.

Resumo Limpo

introduc algorithm learn patientspecif model clinic data predict outcom patientspecif model influenc particular histori symptom laboratori result featur patient case hand contrast common use populationwid model construct perform well averag futur case patientspecif algorithm use markov blanket mb model carri bayesian model averag set model predict outcom patient case hand employ patientspecif heurist locat set suitabl model averag evalu util use local structur represent condit probabl distribut mb model captur addit independ relat among variabl compar typic use represent captur global structur among variabl addit compar perform bayesian model averag model select patientspecif algorithm variant evalu two clinic dataset two outcom result provid support perform algorithm learn patientspecif model can improv use local structur represent mb model perform bayesian model averag

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