Predicting Risk of Multiple Sclerosis Worsening

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969695" target="_blank" >RIV/49777513:23520/23:43969695 - isvavai.cz</a>
Result on the web
<a href="https://ceur-ws.org/Vol-3497/paper-101.pdf" target="_blank" >https://ceur-ws.org/Vol-3497/paper-101.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Predicting Risk of Multiple Sclerosis Worsening
Original language description
This paper describes our participation in the first two tasks of the iDPP@CLEF 2023 challenge focused on providing clinicians with AI-based methods for better prediction of Multiple Sclerosis progression. We evaluate several standard and transformer-based methods, e.g., Random Forest, Gradient Boosting, and SurfTRACE transformer, to address the risk and cumulative probability of Multiple Sclerosis worsening. The considerable performance increase was achieved by (i) hyper-parameter fine-tuning, (ii) validation procedure, and (iii) data pre-processing. The best method based on the Random Forest algorithm scored first place in Task 1 and 2 (sub-task A) with a C-Index of 0.834, and a mean AUROC score of 0.881, respectively, while reducing the runner-up’s error by 16.2% and 2.3%, respectively. Our methods purely designed and optimized for sub-task A and submitted into sub-task B showed considerable robustness towards overfitting on a specific dataset as achieved third and second place and achieving 0.601 C-Index and second in Task 2, sub-task B of 0.607 mean AUROC score.
Czech name
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Czech description
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Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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