Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
J Biomed Inform. 2010 Oct;43(5):669-85. doi: 10.1016/j.jbi.2010.04.009. Epub 2010 May 5.
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.
我们介绍了一种从临床数据中学习患者特异性模型以预测结果的算法。与通常用于构建的旨在平均提高所有未来病例性能的人群范围模型相比，患者特异性模型受到手头患者病例的特定病史、症状、实验室结果和其他特征的影响。患者特异性算法使用马尔可夫毯（MB）模型，对一组模型进行贝叶斯模型平均以预测手头患者病例的结果，并采用患者特异性启发式方法找到一组适合进行平均的模型。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，与通常仅捕获变量之间全局结构的表示相比，该表示捕获了变量之间的额外独立性关系。此外，我们比较了贝叶斯模型平均与模型选择的性能。我们在两个临床数据集上针对两个结果评估了患者特异性算法及其变体。我们的结果提供了支持，即通过使用 MB 模型的局部结构表示和执行贝叶斯模型平均，可以提高学习患者特异性模型的算法的性能。
