Abstract
Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) are chronic diseases that cause progressive or alternating neurological impairments in motor, sensory, visual, and cognitive functions. Affected patients must manage hospital stays and home care while facing uncertainty and significant psychological and economic burdens that also affect their caregivers. To ease these challenges, clinicians need automatic tools to support them in all phases of patient treatment, suggest personalized therapeutic paths, and preemptively indicate urgent interventions.
iDPP@CLEF aims at developing an evaluation infrastructure for AI algorithms to describe ALS and MS mechanisms, stratify patients based on their phenotype, and predict disease progression in a probabilistic, time-dependent manner.
iDPP@CLEF 2022 ran as a pilot lab in CLEF 2022, with tasks related to predicting ALS progression and explainable AI algorithms for prediction. iDPP@CLEF 2023 will continue in CLEF 2023, with a focus on predicting MS progression and exploring whether pollution and environmental data can improve the prediction of ALS progression.
G. Faggioli, A. Guazzo, S. Marchesin, L. Menotti and I. Trescato—These authors contributed equally.
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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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Faggioli, G. et al. (2023). Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2023. In: Arampatzis, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2023. Lecture Notes in Computer Science, vol 14163. Springer, Cham. https://doi.org/10.1007/978-3-031-42448-9_24
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