Learning patient-specific predictive models from clinical data.
Learning patient-specific predictive models from clinical data.
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DOI：
10.1016/j.jbi.2010.04.009
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发表时间：
2010-10
影响因子：
4.5
通讯作者：
Cooper GF
中科院分区：
文献类型：
--
作者：
Visweswaran S;Angus DC;Hsieh M;Weissfeld L;Yealy D;Cooper GF
我们引入了一种算法，用于从临床数据中学习患者特定模型以预测结果。患者特异性模型受特定病史、症状、实验室结果和手头患者病例的其他特征的影响，这与通常使用的人群范围模型形成鲜明对比，后者旨在对所有未来病例平均表现良好。针对患者的算法使用马尔可夫毯子（MB）模型，对一组模型进行贝叶斯模型平均，以预测当前患者病例的结果，并采用针对患者的启发式方法定位一组合适的模型进行平均。我们评估了在MB模型中使用局部结构表示条件概率分布的效用，该表示捕获了变量之间的额外独立关系，而通常使用的表示仅捕获变量之间的全局结构。此外，我们还比较了贝叶斯模型平均和模型选择的性能。患者特异性算法及其变体在两个临床数据集上对两个结果进行了评估。我们的研究结果表明，通过使用MB模型的局部结构表示和贝叶斯模型平均，可以提高学习特定患者模型的算法的性能。
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.
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