The episodic, irregular and asynchronous nature of medical data render them difficult substrates for standard machine learning algorithms. We would like to abstract away this difficulty for the class of time-stamped categorical variables (or events) by modeling them as a renewal process and inferring a probability density over continuous, longitudinal, nonparametric intensity functions modulating that process. Several methods exist for inferring such a density over intensity functions, but either their constraints and assumptions prevent their use with our potentially bursty event streams, or their time complexity renders their use intractable on our long-duration observations of high-resolution events, or both. In this paper we present a new and efficient method for inferring a distribution over intensity functions that uses direct numeric integration and smooth interpolation over Gaussian processes. We demonstrate that our direct method is up to twice as accurate and two orders of magnitude more efficient than the best existing method (thinning). Importantly, the direct method can infer intensity functions over the full range of bursty to memoryless to regular events, which thinning and many other methods cannot. Finally, we apply the method to clinical event data and demonstrate the face-validity of the abstraction, which is now amenable to standard learning algorithms.

本文提出一种用于医疗事件数据的统计学习方法，它基于时标分类变量（或事件）的更新过程，并推断出在这个过程中调制的概率密度。使用基于高斯过程的数字积分和平滑插值的方法来近似推断强度函数概率密度。这个方法不仅精确度更高，而且还可以用于处理波动型、无记忆型和规则型事件，可以被标准的学习算法调用。在医疗事件数据上的实验证明了这种抽象方法的合理性。

高斯过程调制的更新过程有效推断及应用于医疗事件数据