Intelligent Disease Progression Prediction at CLEF - iDPP@CLEF 2024

 iDPP CLEF Workshop – 11 & 12 September 2024

Agenda

Plenary Lab Overview (20 mins) – Wednesday 11 September 2024, 11:15-12:45 CEST

Session 1 (90 mins) – Thursday 12th September 2024, 11:15-12:45 CEST

Welcome and Introduction (5 mins)

Overview of the Lab (20 mins, including questions)

Overview of iDPP@CLEF 2024: The Intelligent Disease Progression Prediction Challenge
Giovanni Birolo, Pietro Bosoni, Guglielmo Faggioli, Helena Aidos, Roberto Bergamaschi, Paola Cavalla, Adriano Chiò, Arianna Dagliati, Mamede de Carvalho, Giorgio Maria Di Nunzio, Piero Fariselli, Jose Manuel Garcia Dominguez, Marta Gromicho, Alessandro Guazzo, Enrico Longato, Sara C. Madeira, Umberto Manera, Stefano Marchesin, Laura Menotti, Gianmaria Silvello, Eleonora Tavazzi, Erica Tavazzi, Isotta Trescato, Martina Vettoretti, Barbara Di Camillo, Nicola Ferro

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Participant Presentations (20 mins, including questions)

ALSFRS-R Score Prediction for Amyotrophic Lateral Sclerosis
 
Guido Barducci, Flavio Sartori, Giovanni Birolo, Tiziana Sanavia, Piero Fariselli
 

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Predicting Multiple Sclerosis Relapses Using Patient Exposure Trajectories
Pietro Bosoni, Mahin Vazifehdan, Daniele Pala, Eleonora Tavazzi, Roberto Bergamaschi, Riccardo Bellazzi, Arianna Dagliati

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Using Wearable and Environmental Data to Improve the Prediction of Amyotrophic Lateral Sclerosis and Multiple Sclerosis Progression: an Explorative Study
Elena Marinello, Alessandro Guazzo, Enrico Longato, Erica Tavazzi, Isotta Trescato, Martina Vettoretti, Barbara Di Camillo

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Lunch Break, 12:45-14:00 CEST

Session 2 (90 mins) – Thursday 12th September 2024, 14:00-15:30 CEST

Participant Presentations (20 mins, including questions)

Predicting the Functional Rating Scale and Self-Assessment Status of ALS Patients with Sensor Data
Andreia S. Martins, Daniela M. Amaral, Eduardo N. Castanho, Diogo F. Soares, Ruben Branco, Sara C. Madeira, Helena Aidos

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Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data
 
Ritesh Mehta, Aleksandar Pramov, Shashank Verma

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UBCS at IDPP: Predicting Patient Self-Assessment Score from Sensor Data using Machine Learning Algorithms
 
Chibuzor Chukwuemeka Okere, Edwin Thuma, Gontlafetse Mosweunyane
 

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BIT.UA at iDPP: Predictive Analytics on ALS Disease Progression Using Sensor Data with Machine Learning
Jorge Miguel Silva, José Luis Oliveira

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Discussion and closing (5 mins)

Motivation

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.

Important dates

  • Registration closes: April 22, 2024
  • Runs submission deadline: May 6, 2024
  • Evaluation results out: May 20, 2024
  • Participant and position paper submission deadline: May 31, 2024
  • Notification of acceptance for participant and position papers: June 24, 2024
  • Camera-ready participant papers submission: July 8, 2024
  • iDPP CLEF Workshop: September 9-12, 2024 during the CLEF Conference

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.

Tasks

Overall iDPP is targeting two kinds of activities: (a) tasks on disease progression prediction; (b) position papers on the impact of the exposure to pollutants on the prediction algorithms. Overall, this mix will provide participants with the opportunity to make some hands-on experience with these data and provide feedback about the task design as well as to brainstorm on how to evaluate this kind of algorithms and, in particular, assess their explainability. 
In this iteration of iDPP@CLEF we will focus on MS for the prediction tasks and on ALS for the position papers task.

In particular, we offer the following activities:

Task 1 – Predicting ALSFRS-R score from sensor data (ALS)

It will focus on predicting the ALSFRS-R score (ALS Functional Rating Scale – Revised), assigned by medical doctors roughly every three months, from the sensor data collected via the app.
The ALSFRS-R score is a somehow “subjective” evaluation 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 (ALS)

It will focus on predicting the self-assessment score assigned by patients from the sensor data collected via the app.

If the self-assessment performed by patients, more frequently than the assessment performed by medical doctors every three months or so, can be reliably predicted by sensor and app data, we can imagine a proactive application which, monitoring the sensor data, alerts the patient if an assessment is needed.

Task 3 – Predicting relapses from EDDS sub-scores and environmental data (MS)

It will focus on predicting a relapse using environmental data and EDSS (Expanded Disability Status Scale) sub-scores.

This task will allow us to assess if exposure to different pollutants is a useful variable in predicting a relapse.

Information on the Datasets

We will provide prospective, fully anonymized MS and ALS clinical data including demographic and clinical characteristics as well as environmental and sensor data, coming from clinical trials currently running at institutions in Italy, Portugal, and Spain.
 
For Task 1 and Task 2, we will release a brand-new dataset consisting of 100 ALS patients that were followed for up to 18 months and whose progression was tracked by regular clinical evaluations. Note that even if the absolute number of patients might not seem high, this is a very rich dataset due to the daily collection of sensor data, with tens of thousands of data points in total.
 
For Task 3, we will re-use part of the MS dataset developed in iDPP@CLEF 2023, consisting of about 400 patients over a period of roughly 10 years, and will extend it with environmental and pollution data
Register Here
Participation Guidelines

Organizers

  • Helena Aidos, University of Lisbon, Portugal
  • Roberto Bergamaschi, University of Pavia, Italy
  • Paola Cavalla, “Città della Salute e della Scienza”, Turin, Italy
  • Adriano Chio’, University of Turin, Italy
  • Arianna Dagliati, University of Pavia, Italy
  • Barbara Di Camillo, University of Padua, Italy
  • Mamede Alves de Carvalho, University of Lisbon, Portugal
  • Nicola Ferro, University of Padua, Italy
  • Piero Fariselli, University of Turin, Italy
  • Jose Manuel García Dominguez, Gregorio Marañon Hospital in Madrid, Spain
  • Sara C. Madeira, University of Lisbon, Italy
  • Eleonora Tavazzi, IRCCS Foundation C. Mondino in Pavia, Italy
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