Multi-Source Contextual Collaborative Recommendation for Medicine
Zheng Zhi, Xu Tong, Qin Chuan, Liao Xiangwen, Zheng Yi, Liu Tongzhu, Tong Guixian
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Recent years have witnessed the accumulation of electronic medical records (EMR), as well as the rapid development of data analytics techniques, which highly support the intelligent medical services, e.g., automatic diagnosis and medicine recommendation. Unfortunately, due to the simplicity of general EMR, the diagnosis model could be easily disturbed by common diseases or symptoms, thus fine-grained prescription with personalized focalization will hardly be achieved. At the same time, we realize that some related context information, e.g., personalized information like age and sexuality, treatment records like examinations, and external information like weather and temperature, could all benefit the diagnosis and medicine recommendation task. However, these information could not be effectively extracted and integrated by current techniques, which constrains the performance of medicine recommendation. To that end, in this paper, we propose a comprehensive framework based on the collaborative awareness of multi-source context information. Specifically, we first utilize the bag-of-words model to process the EMR and related context records. Along this line, a LDA-based contextual collaborative model called Medicine-LDA has been designed to integrate the multi-source information, while at the same time, alleviate the problem of combination explosion of context information. Extensive experiments on the real-world data set from a first-rate hospital demonstrate the effectiveness of our solution.