Dr. Zhengxing Huang of Zhejiang University (Hangzhou, China)
Predictive monitoring of clinical pathways.
Accurate and timely monitoring, as a key aspect of clinical pathway (CP) management, provides crucial information to medical staff and hospital managers for determining the efficient medical service delivered to individual patients, and for promptly handling unusual treatment behaviors in CPs. In many applications, CP monitoring is performed in a reactive manner, e.g., variant treatment events are detected only after they have occurred in CPs. Alternatively, this study systematically presents a learning framework for predictive monitoring of CPs. The proposed framework is composed of both offline analysis and online monitoring phases. In the offline phase, a particular probabilistic topic model, i.e., treatment pattern model (TPM), is generated from electronic medical records to describe essential/critical medical behaviors of CPs. Using TPM-based measures as a descriptive vocabulary, online monitoring of CPs can be provided for ongoing patient-care journeys. Specifically, two typical predictive monitoring services, i.e., unusual treatment event prediction and clinical outcome prediction, are presented to illustrate how the potential of the proposed framework can be exploited to provide online monitoring services from both internal and external perspectives of CPs. Extensive evaluation on a real clinical data-set, typically missing from other work, demonstrates the efficacy and generality of the proposed framework for surveillance-based CP management in a predictive manner.