Abstract
Clinical Pathway (CP) represents the best practice of treatment process management for inpatients with specific diagnosis, and a treatment process can be divided into several stages, usually in units of days. With the explosion of medical data, CP analysis is receiving increasing attention, which provides important support for CP design and optimization. However, these data-driven researches often suffer from the high complexity of medical data, so that a proper representation of medical features is necessary. Most of existing representation learning methods in healthcare domain focus on outpatient data, which get weak performance and interpretability when adopted for CP analysis. In this paper, we propose a new representation, RoMCP, which can capture both diagnosis information and temporal relations between days. The learned diagnosis embedding grasps the key factors of the disease, and each day embedding is determined by the diagnosis together with the preorder days. We evaluate RoMCP on real-world dataset with 538K inpatient visits for several typical CP analysis tasks. Our method demonstrates significant improvement on performance and interpretation.
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- 1.
A patient who stays in a hospital while receiving medical care or treatment. In general, it takes up more resources and cost compared to outpatients.
- 2.
Doctors usually make a prescription with multiple events together. There is no strict temporal relations between these events.
- 3.
Outpatient contains several visits. It is common that the events between sequential visits are quite different, due to the different diagnosis.
- 4.
Some visits may have more than one diagnosis. While for CP, we only concern the first diagnosis, which largely determines the treatment strategy.
- 5.
It refers to the 10th revision of the International Statistical Classification of Diseases and Related Health Problems that listed by the World Health Organization. In our dataset, an Chinese version is used for NRCMS.
- 6.
- 7.
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Acknowledgments
This work was supported by The National Key Technology R&D Program (No. 2015BAH14F02), and Project 61325008 (Mining and Management of Large Scale Process Data) supported by NSFC.
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Xu, X., Wang, Y., Jin, T., Wang, J. (2018). Learning the Representation of Medical Features for Clinical Pathway Analysis. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10828. Springer, Cham. https://doi.org/10.1007/978-3-319-91458-9_3
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