In March 2015 I got my Master’s degree in Biomedical Engeneering at the Eindhoven University of Technology (TU/e) in the field of Regenerative Medicine and Tissue Engineering. I also studied at Leiden University where I obtained my Master’s degree in Medicine in 2008. With this background I am able to bridge the gap between technology and patient. It inspires me to combine multiple disciplines and work with experts from different professional backgrounds.
Currently, I work as PhD student at the department of Clinical Chemistry and Laboratory Medicine at the Catharina Hospital in Eindhoven. My PhD project is part of the Data Science Flagship, a collaboration between TU/e and Philips Research (IMPULS 2.0). The aim of the project is to improve the outcome of cardiac resynchronization therapy (CRT) using hospital data and/or personal health devices by discriminating beforehand between responders and non-responders of CRT.
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.
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