Early Prediction of Multiple Sclerosis Disability Progression via Multimodal Foundation Model Benchmarks

• URL: http://arxiv.org/abs/2506.14986v1
• Date: Tue, 17 Jun 2025 21:37:45 GMT
• Title: Early Prediction of Multiple Sclerosis Disability Progression via Multimodal Foundation Model Benchmarks
• Authors: Maxime Usdin, Lito Kriara, Licinio Craveiro,
• Abstract summary: This work predicts 48- and 72-week disability using sparse clinical data and daily digital Floodlight data from the CONSONANCE clinical trial.<n>We employed state-of-the-art foundation models (FMs), a custom multimodal attention-based transformer, and machine learning methods.<n>Our findings demonstrate the promise of FMs and multimodal approaches to extract predictive signals from complex and diverse clinical and digital life sciences data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Early multiple sclerosis (MS) disability progression prediction is challenging due to disease heterogeneity. This work predicts 48- and 72-week disability using sparse baseline clinical data and 12 weeks of daily digital Floodlight data from the CONSONANCE clinical trial. We employed state-of-the-art tabular and time-series foundation models (FMs), a custom multimodal attention-based transformer, and machine learning methods. Despite the difficulty of early prediction (AUROC 0.63), integrating digital data via advanced models improved performance over clinical data alone. A transformer model using unimodal embeddings from the Moment FM yielded the best result, but our multimodal transformer consistently outperformed its unimodal counterpart, confirming the advantages of combining clinical with digital data. Our findings demonstrate the promise of FMs and multimodal approaches to extract predictive signals from complex and diverse clinical and digital life sciences data (e.g., imaging, omics), enabling more accurate prognostics for MS and potentially other complex diseases.

Related papers

• NeuroMoE: A Transformer-Based Mixture-of-Experts Framework for Multi-Modal Neurological Disorder Classification [3.5313393560458826]
  Deep Learning has recently emerged as a powerful tool for extracting meaningful patterns from medical data to aid in diagnosis.<n>We propose a novel transformer-based Mixture-of-Experts (MoE) framework for classifying neurological disorders.<n>Our framework achieves a validation accuracy of 82.47%, outperforming baseline methods by over 10%.
  arXiv  Detail & Related papers  (2025-06-17T20:40:06Z)
• Continually Evolved Multimodal Foundation Models for Cancer Prognosis [50.43145292874533]
  Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates.<n>Previous studies have integrated diverse data modalities, such as clinical notes, medical images, and genomic data, leveraging their complementary information.<n>Existing approaches face two major limitations. First, they struggle to incorporate newly arrived data with varying distributions into training, such as patient records from different hospitals.<n>Second, most multimodal integration methods rely on simplistic concatenation or task-specific pipelines, which fail to capture the complex interdependencies across modalities.
  arXiv  Detail & Related papers  (2025-01-30T06:49:57Z)
• Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs [6.5527554277858275]
  We present the first causal temporal framework to model the continuous temporal evolution of disease progression via Neural Differential Equations (NSDE) Our results present the first successful uncertainty-based causal Deep Learning (DL) model to accurately predict future patient MS disability (e.g. EDSS) and treatment effects.
  arXiv  Detail & Related papers  (2024-06-18T17:22:55Z)
• DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency [18.291267748113142]
  We propose DrFuse to achieve effective clinical multi-modal fusion. We address the missing modality issue by disentangling the features shared across modalities and those unique within each modality. We validate the proposed method using real-world large-scale datasets, MIMIC-IV and MIMIC-CXR.
  arXiv  Detail & Related papers  (2024-03-10T12:41:34Z)
• Multimodal Clinical Trial Outcome Prediction with Large Language Models [28.95412904299012]
  We propose a multimodal mixture-of-experts (LIFTED) approach for clinical trial outcome prediction.<n>LIFTED unifies different modality data by transforming them into natural language descriptions.<n>Then, LIFTED constructs unified noise-resilient encoders to extract information from modal-specific language descriptions.
  arXiv  Detail & Related papers  (2024-02-09T16:18:38Z)
• XAI for In-hospital Mortality Prediction via Multimodal ICU Data [57.73357047856416]
  We propose an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data. We employ multimodal learning in our framework, which can receive heterogeneous inputs from clinical data and make decisions. Our framework can be easily transferred to other clinical tasks, which facilitates the discovery of crucial factors in healthcare research.
  arXiv  Detail & Related papers  (2023-12-29T14:28:04Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics [63.106382317917344]
  We report a Transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary diseases.
  arXiv  Detail & Related papers  (2023-06-01T16:23:47Z)
• Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression [46.394865849252696]
  Multiple sclerosis is a disease that affects the brain and spinal cord, it can lead to severe disability and has no known cure. In a recent paper it was shown that disease progression can be predicted effectively using performance outcome measures and demographic data. We benchmark four continuous time models using a publicly available multiple sclerosis dataset. We find that the best continuous model is often able to outperform the best benchmarked discrete time model.
  arXiv  Detail & Related papers  (2023-02-15T18:45:32Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks [62.9447303059342]
  We show the importance of this problem in medical community. We present a modification of Bidirectional Representations from Transformers (BERT) model for classification sequence. We use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits.
  arXiv  Detail & Related papers  (2020-07-15T09:22:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.