Abstract
For clinical processes, meaningful variations may be related to care performance or even the patient survival. It is imperative that the variations be predicted timely so that the patient care “journey” can be more adaptive and efficient. This study addresses the question of how to predict variations in clinical processes. Given the assumption that a clinical case with low appropriateness between its specific patient state and its’ applied medical intervention is more likely to be a variation than other cases, this paper proposes a method to construct an appropriateness measure model based on historical clinical cases so as to predict such variations in future cases of clinical processes. The proposed method is demonstrated on a real life data set from the Chinese Liberation Army General Hospital. The experimental results confirm the given assumption and indicate the feasibility of the proposed method.
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© 2011 Springer-Verlag Berlin Heidelberg
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Huang, Z., Lu, X., Gan, C., Duan, H. (2011). Variation Prediction in Clinical Processes. In: Peleg, M., Lavrač, N., Combi, C. (eds) Artificial Intelligence in Medicine. AIME 2011. Lecture Notes in Computer Science(), vol 6747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22218-4_36
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DOI: https://doi.org/10.1007/978-3-642-22218-4_36
Publisher Name: Springer, Berlin, Heidelberg
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