Date
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Speaker
Cécile Trottet
Interpretable ML models for patient trajectories with applications to chronic rheumatic diseases
Rheumatic diseases like rheumatoid arthritis (RA) and systemic sclerosis (SSc) are complex, often causing long-term disability. The challenge in predicting disease progression lies in the data's longitudinal, sparse, heterogeneous, and high-dimensional nature. Cécile Trottet (PhD Candidate in Clinical Data Science at the University of Zurich, KrauthammerLab) will explore machine learning methods that interpret and model this complex data effectively, identifying patients with similar disease progression patterns and predicting future disease activity in RA and organ involvement in SSc.