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As a data-driven healthcare service, automatic treatment regimen design has great potential to improve healthcare efficiency and quality. However, it is a nontrivial endeavor to develop such a healthcare service due to two major challenges: 1) the treatment records are complex data objects consisting of various semantic and temporal information, and 2) the treatment outcome usually depends on a large number of internal and external factors. Because of these difficulties, automatic treatment regimen design is still an open research problem nowadays. To fill this research gap, this paper first formulates a treatment sequence as temporal sets, then provides a novel Extended Jaccard Similarity (EJS) measure to quantify the similarities between treatment sequences. We show that the proposed EJS is a general and effective measure to capture the similarity between two complex temporal sets. Further, we develop an efficient clustering algorithm which can achieve reasonable clustering results with only a portion of the pairwise similarities between treatment sequences and then extract a semantic prototype of the treatment regimen from each cluster of treatment sequences. Finally, we adopt a matrix factorization framework to predict the treatment outcomes by integrating multiple internal and external factors. We conduct comprehensive experiments on Electronic Medical Records (EMRs) of more than 28,000 patients from 14 hospitals. The results demonstrate the effectiveness of our approach and its superiority over the state-of-the-art ones. [ABSTRACT FROM AUTHOR]

Sequential pattern analysis aims at finding statistically relevant temporal structures where the values are delivered in a sequence. With the growing complexity of real-world dynamic scenarios, more and more symbols are often needed to encode the sequential values. This is so-called “curse of cardinality”, which can impose significant challenges to the design of sequential analysis methods in terms of computational efficiency and practical use. Indeed, given the overwhelming scale and the heterogeneous nature of the sequential data, new visions and strategies are needed to face the challenges. To this end, in this paper, we propose a “temporal skeletonization” approach to proactively reduce the cardinality of the representation for sequences by uncovering significant, hidden temporal structures. The key idea is to summarize the temporal correlations in an undirected graph, and use the “skeleton” of the graph as a higher granularity on which hidden temporal patterns are more likely to be identified. As a consequence, the embedding topology of the graph allows us to translate the rich temporal content into a metric space. This opens up new possibilities to explore, quantify, and visualize sequential data. Our approach has shown to greatly alleviate the curse of cardinality in challenging tasks of sequential pattern mining and clustering. Evaluation on a business-to-business (B2B) marketing application demonstrates that our approach can effectively discover critical buying paths from noisy customer event data. [ABSTRACT FROM PUBLISHER]