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Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease whose mechanisms are still fully unclear. Being able to predict ALS prognosis would help in improving the patients' quality of life and support clinicians in planning treatments. On the one hand, most of the modeling approaches to ALS miss to catch the evolving nature of the disease; on the other, Process Mining (PM) comprehends techniques useful to generally describe processes, but often misses methods to reveal statistically significant differences in the mined pathways. In this paper, we investigate ALS evolution using PM techniques enriched to easily mine processes and, at the same time, automatically reveal how the pathways differentiate according to patients' characteristics.
University of Padova, Italy
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