Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining

• Published in: Journal of healthcare engineering 2017-01, Vol.2017 (2017), p.1-13
• Main Authors: Wang, Jianmin, Wei, Zhijie, Jin, Tao, Xu, Xiao
• Format: Article
• Language: English
• Subjects: Algorithms; Cerebral Hemorrhage - diagnosis; Cerebral Hemorrhage - therapy; Cost control; Critical Pathways; Data Mining; Humans; Medical care, Cost of; Models, Statistical; Quality Assurance, Health Care
• ISSN: 2040-2295; 2040-2309
• DOI: 10.1155/2017/5208072

Clinical pathways are widely used around the world for providing quality medical treatment and controlling healthcare cost. However, the expert-designed clinical pathways can hardly deal with the variances among hospitals and patients. It calls for more dynamic and adaptive process, which is derived from various clinical data. Topic-based clinical pathway mining is an effective approach to discover a concise process model. Through this approach, the latent topics found by latent Dirichlet allocation (LDA) represent the clinical goals. And process mining methods are used to extract the temporal relations between these topics. However, the topic quality is usually not desirable due to the low performance of the LDA in clinical data. In this paper, we incorporate topic assignment constraint and topic correlation limitation into the LDA to enhance the ability of discovering high-quality topics. Two real-world datasets are used to evaluate the proposed method. The results show that the topics discovered by our method are with higher coherence, informativeness, and coverage than the original LDA. These quality topics are suitable to represent the clinical goals. Also, we illustrate that our method is effective in generating a comprehensive topic-based clinical pathway model.