Abstract：With the explosion of the amount of medical data, data-driven medical analyses are receiving increasing attention. Proper representation of medical activities is crucial for such analyses. However, most existing representations are designed without considering the temporality and numerical sensitivity of medical data, which limits the performance and interpretability of the analysis tasks. This paper presents a representation learning approach for medical activities that is enhanced by topical modeling for inpatient data. The approach leverages the temporal relations between activities and the topic assignment to construct a multilayer perceptron model. Evaluations using large real data sets demonstrate that this approach significantly improves three typical medical analysis tasks, while providing medical interpretations.
徐啸, 王灜, 金涛, 王建民. 基于主题模型加强的医疗活动表征学习方法[J]. 清华大学学报（自然科学版）, 2019, 59(3): 169-177.
XU Xiao, WANG Ying, JIN Tao, WANG Jianmin. Representation learning approach for medical activities enhanced by topical modeling. Journal of Tsinghua University(Science and Technology), 2019, 59(3): 169-177.
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2019, Vol. 59 
