Third-party cyber evaluations involving OpenAI models
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AI Research
arXiv CS.AI·Jun 10·5 min read
LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts
TL;DR
Multimodal clinical learning struggles with missing data and the evolving nature of patient conditions over time. LongMoE, or Longitudinal Mixture-of-Experts, is a framework that integrates context-aware imputation and trajectory modeling to address these issues.
✦ Why It Matters
Engineers can leverage LongMoE to build more resilient models for clinical data analysis that handle missing information effectively.
Key Takeaways
- Implement LongMoE for robust clinical data analysis.
- Utilize context-aware imputation to handle missing modalities.
- Incorporate trajectory modeling for evolving patient conditions.
- Test models on diverse datasets like ADNI and MIMIC-IV.
Full Summary
Multimodal clinical learning involves integrating various types of patient data, such as imaging and health records, but faces challenges like missing data and the need to consider how a patient's condition changes over time. LongMoE, or Longitudinal Mixture-of-Experts, is a novel framework designed to tackle these challenges simultaneously.
It features a context-aware imputation module to fill in missing data, an attentional tokenization module to analyze temporal patterns in irregular patient visits, and a trajectory-aware encoder to model disease progression. Additionally, it employs context-conditioned Sparse MoE routing for selecting patient-specific experts.
Experiments conducted on datasets like ADNI, OASIS-3, and MIMIC-IV demonstrate that LongMoE significantly improves performance under conditions of missing or weak data while remaining effective when all modalities are available. This advancement lays a strong foundation for future research in longitudinally-aware multimodal clinical learning.