Personalized Clinical Pathway Recommendation via Attention Based Pre-training

Clinical pathways are standardized, evidence-based multidisciplinary management plans. Many countries propose their national clinical pathways to improve the quality of care, reduce variation in clinical practice, and increase the efficient use of healthcare resources. Nevertheless, clinical pathways are typically not prescriptive, and the patient’s care journey is an individual one, therefore how to handle the variances of clinical pathways is an important issue. Previous methods construct clinical pathway recommendation models either using national standard clinical pathways to obtain the guidance, or using real-world clinical datasets to obtain clinical experience. However, few research tries to use both of them. This will result in existing algorithms that cannot accurately recommend personalized clinical pathway. To overcome the above problems, we propose P ersonalized C linical P athway Rec ommendation(PCPRec). On the one hand, to obtain general clinical pathway recommendations, we built a novel module to pre-train the self-attention model based on the national standard clinical pathway. So that we can use it as a guide to enhance the accuracy of recommending personalized clinical pathway. On the other hand, we obtain the patient’s treatment history sequence from real-world clinical datasets, and use the self-attention model for training. The purpose is to learn from the experience of the relationship between clinical items to meet patient’s individual needs. Extensive experimental results show that the proposed model achieves the best results compared to state-of-art methods on benchmark datasets.