Abstract
Amyotrophic Lateral Sclerosis (ALS) is a severe chronic disease characterized by progressive or alternate impairment of neurological functions, characterized by high heterogeneity both in symptoms and disease progression. As a consequence its clinical course is highly uncertain, challenging both patients and clinicians. Indeed, patients have to manage alternated periods in hospital with care at home, experiencing a constant uncertainty regarding the timing of the disease acute phases and facing a considerable psychological and economic burden that also involves their caregivers. Clinicians, on the other hand, need tools able to support them in all the phases of the patient treatment, suggest personalized therapeutic decisions, indicate urgently needed interventions. The goal of
is to design and develop an evaluation infrastructure for AI algorithms able to:
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1.
better describe disease mechanisms;
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2.
stratify patients according to their phenotype assessed all over the disease evolution;
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3.
predict disease progression in a probabilistic, time dependent fashion.
A. Guazzo and I. Trescato—These authors contributed equally.
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Notes
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Death is considered a competing event since a patient might incur death before experiencing the event of interest; the models should account for that.
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For the tasks 1c and 2c, death is not a competing event anymore but the focus of the models’ predictions.
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Acknowledgments
The work reported in this paper has been partially supported by the BRAINTEASER (https://brainteaser.health/) project (contract n. GA101017598), as a part of the European Union’s Horizon 2020 research and innovation programme.
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Guazzo, A. et al. (2022). Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2022. In: Barrón-Cedeño, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2022. Lecture Notes in Computer Science, vol 13390. Springer, Cham. https://doi.org/10.1007/978-3-031-13643-6_25
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