DOI: 10.5281/zenodo.7477918
title: iDPP@CLEF 2022 - Participants' repositories for the Intelligent Disease Prediction Progression Challenge

description: iDPP@CLEF 2022 (Intelligent Disease Progression Prediction at CLEF) is a challenge organised by the BRAINTEASER Horizon 2020 project and co-located with CLEF 2022 (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<strong> disease mechanisms</strong>. <strong>Stratify patients </strong>according to their phenotype assessed all over the disease evolution. <strong>Predict disease progression</strong> in a probabilistic, time dependent fashion. iDPP@CLEF 2022 offered the following tasks: <strong>Pilot Task 1 – Ranking Risk of Impairment</strong>: It focuses on ranking of patients based on the risk of impairment in specific domains. More in detail, we will use the ALSFRS-R scale to monitor speech, swallowing, handwriting, dressing/hygiene, walking and respiratory ability in time and will ask participants to rank patients based on time to event risk of experiencing impairment in each specific domain. <strong>Pilot Task 2 – Predicting Time of Impairment</strong>: It refines Task 1 asking participants to predict when specific impairments will occur (i.e. in the correct time-window). In this regard, we assess model calibration in terms of the ability of the proposed algorithms to estimate a probability of an event close to the true probability within a specified time-window. <strong>Position Papers Task 3 – Explainability of AI algorithms</strong>: We call for proposals of different visualization frameworks able to show the multivariate nature of the data and the model predictions in an explainable, possibly interactive, way. This dataset contains the repositories of the participants to iDPP@CLEF 2022. These repositories contain the output, i.e. the predictions, produced by the participating systems as well as the performance scores for those systems. For additional information about iDPP@CLEF 2022, please see: Guazzo, A., Trescato, I., Longato, E., Hazizaj, E., Dosso, D., Faggioli, G., Di Nunzio, G. M., Silvello, G., Vettoretti, M., Tavazzi, E., Roversi, C., Fariselli, P., Madeira, S. C., de Carvalho, M., Gromicho, M., Chiò, A., Manera, U., Dagliati, A., Birolo, G., Aidos, H., Di Camillo, B., and Ferro, N. (2022). Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2022. In Barr ́on-Cedeno, A., Da San Martino, G., Degli Es- posti, M., Sebastiani, F., Macdonald, C., Pasi, G., Hanbury, A., Potthast, M., Faggioli, G., and Ferro, N., editors, <em>Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the Thirteenth International Conference of the CLEF Association (CLEF 2022)</em>, pages 395–422. Lecture Notes in Computer Science (LNCS) 13390, Springer, Heidelberg, Germany. Guazzo, A., Trescato, I., Longato, E., Hazizaj, E., Dosso, D., Faggioli, G., Di Nunzio, G. M., Silvello, G., Vettoretti, M., Tavazzi, E., Roversi, C., Fariselli, P., Madeira, S. C., de Carvalho, M., Gromicho, M., Chiò, A., Manera, U., Dagliati, A., Birolo, G., Aidos, H., Di Camillo, B., and Ferro, N. (2022). Overview of iDPP@CLEF 2022: The Intelligent Disease Progression Prediction Challenge. In Faggioli, G., Ferro, N., Hanbury, A., and Potthast, M., editors, <em>CLEF 2022 Working Notes</em>, pages 1130– 1210. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073. http://ceur-ws.org/Vol-3180/.
description: The repositories contained in this dataset are also available online at: https://bitbucket.org/brainteaser-health/
description: {"references": ["Branco, R., Soares, D., Martins, A. S., Auletta, E., Castanho, E. N., Nunes, S., Serrano, F., Sousa, R. T., Pesquita, C., Madeira, S. C., and Aidos, H. (2022). Hierarchical Modelling for ALS Prognosis: Predicting the Pro- gression Towards Critical Events. In Faggioli, G., Ferro, N., Hanbury, A., and Potthast, M., editors, CLEF 2022 Working Notes, pages 1211\u2013 1227. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073. http://ceur-ws.org/Vol-3180/.", "Mannion, A., Chevalier, T., Schwab, D., and Goeuriot, L. (2022). Predicting the Risk of & Time to Impairment for ALS patients. In Faggioli, G., Ferro, N., Hanbury, A., and Potthast, M., editors, CLEF 2022 Working Notes. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073. http://ceur-ws.org/Vol-3180/.", "Pancotti, C., Birolo, G., Sanavia, T., Rollo, C., and Fariselli, P. (2022). Multi-Event Survival Prediction for Amyotrophic Lateral Sclerosis. In Faggioli, G., Ferro, N., Hanbury, A., and Potthast, M., editors, CLEF 2022 Working Notes, pages 1269\u20131276. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073. http://ceur-ws.org/Vol-3180/.", "Trescato, I., Guazzo, A., Longato, E., Hazizaj, E., Roversi, C., Tavazzi, E., Vettoretti, M., and Di Camillo, B. (2022). Baseline Machine Learning Ap- proaches To Predict Amyotrophic Lateral Sclerosis Disease Progression. In Faggioli, G., Ferro, N., Hanbury, A., and Potthast, M., editors, CLEF 2022 Working Notes, pages 1277\u20131293. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073. http://ceur-ws.org/Vol-3180/."]}