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Published in:
2023 | OriginalPaper | Chapter
iDPP@CLEF 2023: The Intelligent Disease Progression Prediction Challenge
Authors : Helena Aidos, Roberto Bergamaschi, Paola Cavalla, Adriano Chiò, Arianna Dagliati, Barbara Di Camillo, Mamede Alves de Carvalho, Nicola Ferro, Piero Fariselli, Jose Manuel García Dominguez, Sara C. Madeira, Eleonora Tavazzi
Published in: Advances in Information Retrieval
Publisher: Springer Nature Switzerland
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Abstract
Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) are chronic diseases characterized by progressive or alternate impairment of neurological functions (motor, sensory, visual, cognitive). 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 iDPP@CLEF is to design and develop an evaluation infrastructure for AI algorithms able to: iDPP@CLEF run as a pilot lab in CLEF 2022, offering tasks on the prediction of ALS progression and a position paper task on explainability of AI algorithms for prediction; 5 groups submitted a total of 120 runs and 2 groups submitted position papers.
1.
better describe disease mechanisms;
2.
stratify patients according to their phenotype assessed all over the disease evolution;
3.
predict disease progression in a probabilistic, time dependent fashion.
iDPP@CLEF will continue in CLEF 2023, focusing on the prediction of MS progression and exploring whether pollution and environmental data can improve the prediction of ALS progression.