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Discovery of medical pathways considering complications

Published: 01 March 2023 Publication History

Abstract

Many medical solutions exist in the vast medical logs, and how to use them to make precise recommendations for medical pathways has become a current research hotspot. This paper proposed an algorithmic framework that combines trace clustering, process discovery, and neural network techniques to discover models of medical pathways considering complications from medical logs. First, trace clustering divided the treatment pathways set in medical logs into multiple subsets with similar behaviours. Then multiple streamlined process models were mined by process discovery techniques. The reasonable medical pathways were extracted from each process model. A cluster of trained neural network models was used to determine the case characteristic labels and obtain the attention of events in the medical pathways. The output was finally integrated to generate a model of medical pathways considering complications. The results of the experiments using the Sepsis Cases dataset showed that the average simplicity of the generated process models was 0.695, the average accuracy of the neural network models was 93.3%, and the medical pathways model score was about 0.879.

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        Published In

        cover image Computers and Electrical Engineering
        Computers and Electrical Engineering  Volume 106, Issue C
        Mar 2023
        738 pages

        Publisher

        Pergamon Press, Inc.

        United States

        Publication History

        Published: 01 March 2023

        Author Tags

        1. Trace clustering
        2. Process discovery
        3. Neural network
        4. Complication
        5. Medical pathway

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