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A Clinical Pathway Model for Severe Stroke Rehabilitation based on Process Mining
All Authors
Abstract:
Cerebral
stroke, as a disease resulting from cerebral circulatory disorders,
continues to have a stubbornly high incidence rate. With the ongoing
advancements in rehabilitation technology, the number of rehabilitation
programs concerning cerebral stroke is gradually increasing. However,
there remains a deficiency in clinical pathways for the rehabilitation
of severe stroke, resulting in an absence of unified standards to
delineate the rehabilitation protocols and expected outcomes at each
stage. Therefore, it is imperative to explore clinical pathways for the
rehabilitation of severe stroke via advanced techniques. This study
employs process mining techniques to analyze the rehabilitation process
of severe stroke patients based on the collected patient data. Following
data normalization and preprocessing, appropriate process mining
algorithms are selected to establish clinical pathway models for severe
stroke rehabilitation. Fuzzy models in process mining are utilized to
explore the clinical processes associated with severe stroke patients
through specific rehabilitation projects. From the output process model,
it can be observed that the patient's rehabilitation process generally
commences with cardiovascular and pulmonary physical therapy, as well as
Neurophysical therapy. Multiple treatment methods are employed
simultaneously, culminating in life-oriented occupational therapy. The
entire rehabilitation process is adaptive, evolving with the patient's
condition.
Published in: 2024 World Rehabilitation Robot Convention (WRRC)
Date of Conference: 23-26 August 2024
Date Added to IEEE Xplore: 01 October 2024
ISBN Information:
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