Multiple Sclerosis (MS) is the most frequent non-traumatic debilitating neurological disease. It is usually diagnosed based on clinical observations and supporting data from auxiliary procedures. However, its course is extremely unpredictable, and traditional statistical survival models fail to perform reliably on longitudinal data. An efficient and precise prognosis model of patient-specific MS time-to-event distributions is needed to aid in joint decision-making in subsequent treatment and care. In this work, we aim to estimate the survival function to predict MS disability progression based on discrete longitudinal reaction time trajectories and related clinical variables. To this end, we initially preprocess two sets of measurements obtained from the same cohort of patients. One set comprises the patients’ reaction trajectories recorded during computerized tests, while the other set involves assessing their disability progression and extracting practical clinical information. Then we propose our deep survival model for discovering the connections between temporal data and the potential risk. The model is optimised over the sum of three losses, including longitudinal loss, survival loss and consistent loss. We evaluate our model against other machine learning methods on the same dataset. The experimental results demonstrate the advantage of our proposed deep learning model and prove that such computerized measurements can genuinely reflect the disease stage of MS patients and provide a second opinion for prognosticating their disability progression.

Original languageEnglish
Title of host publicationPredictive Intelligence in Medicine - 6th International Workshop, PRIME 2023 Held in Conjunction with MICCAI 2023 Vancouver, BC, Canada, October 8, 2023 Proceedings
EditorsIslem Rekik, Ehsan Adeli, Sang Hyun Park, Celia Cintas, Ghada Zamzmi
Place of PublicationCham Switzerland
Number of pages12
ISBN (Electronic)9783031460050
ISBN (Print)9783031460043
Publication statusPublished - 2023
EventInternational Workshop on Predictive Intelligence In Medicine 2023 - Vancouver, Canada
Duration: 8 Oct 20238 Oct 2023
Conference number: 6th
https://link.springer.com/book/10.1007/978-3-031-46005-0 (Proceedings)
https://basira-lab.com/prime-miccai-2023/ (Website)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Workshop on Predictive Intelligence In Medicine 2023
Abbreviated titlePRIME 2023
Internet address


  • Disability progression
  • Multiple sclerosis
  • Survival analysis
  • temporal

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