iDPP@CLEF 2024 - Participants' repositories for the Intelligent Disease Prediction Progression Challenge

iDPP@CLEF 2024 (Intelligent Disease Progression Prediction at CLEF) is a challenge organized by the BRAINTEASER Horizon 2020 project and co-located with CLEF 2024 (Conference and Labs of the Evaluation Forum).

BRAINTEASER is a data science project that seeks to exploit the value of big data, including those related to health, lifestyle habits, and environment, to support patients with amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) and their clinicians. Taking advantage of cost-efficient sensors and apps, BRAINTEASER will integrate large, clinical datasets that host both patient-generated and environmental data.

The goal of iDPP@CLEF is to design and develop an evaluation infrastructure for AI algorithms able to:

• Better describe disease mechanisms.
• Stratify patients according to their phenotype assessed all over the disease evolution.
• Predict disease progression in a probabilistic, time-dependent fashion.

iDPP@CLEF 2024 built upon iDPP@CLEF 2023 and iDPP@CLEF 2022, expanding the tasks of the previous edition and providing novel tasks.

iDPP@CLEF 2023 offered the following tasks:

• Task 1: Predicting ALSFRS-R Score from Sensor Data (ALS) It focuses on predicting the twelve scores of the ALSFRS-R (ALS Functional Rating Scale - Revised), assigned by medical doctors roughly every three months, from the sensor data collected via the app. The ALSFRS-R is a somehow “subjective” evaluation usually performed by a medical doctor and this task will help in answering a currently open question in the research community, i.e. whether it could be derived from objective factors.
• Task 2: Predicting Patient Self-assessment Score from Sensor Data: It focuses on predicting the self-assessment score assigned by patients from the sensor data collected via the app. Self-assessment scores correspond to each of the ALSFRS-R scores but, while the latter ones are assigned by medical doctors during visits, the former ones are assigned via auto-evaluation by patients themselves using the provided app.
• Task 3: Predicting Relapses from EDDS Sub-scores and Environmental Data (MS): It focuses on predicting a relapse using environmental data and EDSS (Expanded Disability Status Scale) sub-scores. This task allows us to assess if exposure to different pollutants is a useful variable in predicting a relapse.

This dataset contains the repositories of the participants to iDPP@CLEF 2024. These repositories contain the output, i.e. the predictions, produced by the participating systems, the code used to obtain it, and the performance scores for those systems.

For additional information about iDPP@CLEF 2024, please see:

Birolo, G., Bosoni, P., Faggioli, G., Aidos, H., Bergamaschi, R., Cavalla, P., et Al. (2024, September). Intelligent disease progression prediction: Overview of iDPP@ CLEF 2024. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 118-139). Cham: Springer Nature Switzerland.

Birolo, G., Bosoni, P., Faggioli, G., Aidos, H., Bergamaschi, R., Cavalla, P., et Al. (2024). Overview of iDPP@ CLEF 2024: the intelligent disease progression prediction challenge. In Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024), Grenoble, France, September 9th to 12th (Vol. 2024).