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Published on: October 6, 2015
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Leveraging process mining for modeling progression trajectories in amyotrophic lateral sclerosis.
Erica Tavazzi1, Roberto Gatta2, Mauro Vallati3
1Department of Information Engineering, University of Padova, Via Gradenigo 6/b, 35131, Padua, Italy.
BMC Medical Informatics and Decision Making
|February 3, 2023
Summary
Process Mining reveals Amyotrophic Lateral Sclerosis (ALS) progression, showing how patient characteristics influence disease trajectories and outcomes. This aids in understanding ALS evolution for better patient care.
Area of Science:
- Neuroscience
- Data Science
- Medical Informatics
Background:
- Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease with unclear progression mechanisms.
- Predicting ALS prognosis is crucial for improving patient quality of life and clinical treatment planning.
Purpose of the Study:
- To investigate ALS evolution trajectories using Process Mining (PM).
- To reveal how disease pathways differentiate based on patient characteristics.
- To compare disease trajectories between a clinical trial dataset and a real-world clinical register.
Main Methods:
- Utilized Process Mining techniques, including Directly-Follows Graph and CareFlow Miner, on the PRO-ACT dataset.
- Analyzed functional impairment trajectories, focusing on patterns, timing, and probabilities.
- Investigated the impact of patient characteristics (e.g., onset type, age) on disease progression.
- Conducted a comparative analysis between the PRO-ACT and ALS-BS datasets.
Main Results:
- Identified predominant disabilities at different ALS stages, with 48% of patients experiencing Walking/Self-care impairment first.
- Demonstrated that spinal onset increases the risk of initial Walking/Self-care impairment (52% vs. 27%).
- Found that older age at onset correlates with faster progression to death.
- Observed similarities and differences in disease progression between PRO-ACT and ALS-BS populations, likely due to trial design.
Conclusions:
- Process Mining offers an overview of ALS progression scenarios in trial populations and allows preliminary comparison with clinical cohorts.
- Further research will enhance understanding of ALS progression by incorporating more real-world data and expanding event analysis.
