Tag
This paper proposes a structural variant of the Factorial Hidden Markov Model to analyze and interpret disease trajectories in Type 2 diabetes patients using electronic health records, revealing clinically meaningful patterns and progression pathways.
This paper develops a multi-dimensional framework to evaluate discrimination, calibration, interpretability, and algorithmic fairness for machine learning-based type 2 diabetes risk prediction models, revealing significant performance degradation under real-world distribution shifts and biases by age and obesity.
T2D-Bench is a benchmark for evaluating LLM outputs for Type 2 Diabetes using a multi-layer clinical-lifestyle knowledge graph. It reveals that current LLMs fail evidence-path checks in about a third of cases.