We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the Minimal Predictive Temporal Patterns framework to generate a small set of predictive and nonspurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin-induced thrombocytopenia. The results demonstrate the benefit of our approach in efficiently learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems.

中文翻译：

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一种用于电子健康记录数据分类的时间模式挖掘方法

我们研究了从电子健康记录系统中遇到的复杂多变量时间数据中学习分类模型的问题。挑战在于定义一组能够很好地表示数据的时间方面的特征。我们的方法依赖于时间抽象和时间模式挖掘来提取分类特征。时间模式挖掘通常会返回大量的时间模式，其中大部分可能与分类任务无关。为了解决这个问题，我们提出了最小预测时间模式框架来生成一小组预测和非杂散模式。我们将我们的方法应用于预测有发生肝素诱导的血小板减少症风险的患者的真实临床任务。