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20232026
most citedKnowledge-Infused LLM-Powered Conversational Health Agent: A Case Study for Diabetes Patients

4 citations · 5 across the 12 of their papers we have counts for

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cs.LG2026

GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits

Nitish Nagesh, Elahe Khatibi, Thomas Dean Hughes +3

Causal discovery recovers directed structure from observational data and is increasingly used in clinical settings to support mechanism reasoning and fairness audits of predictive…

cs.LG2026

Beyond AHI: An Interpretable Causal-Discovery-Guided Framework for Sleep Recovery in Connected Health

Saba A. Farahani, Elahe Khatibi, Manoj Vishwanath +2

Objective sleep assessment relies on polysomnography (PSG), yet clinical impact is often better reflected in patient-reported outcomes (PROs) such as sleepiness and fatigue. Existi…

cs.LG2026

PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning

Elahe Khatibi, Ziyu Wang, Saba A. Farahani +4

Personalized healthcare decisions require reasoning about how physiological and behavioral variables influence an individual patient over time. Existing temporal causal discovery m…

cs.LG2026

Membership Inference Attacks Expose Participation Privacy in ECG Foundation Encoders

Ziyu Wang, Elahe Khatibi, Ankita Sharma +4

Foundation-style ECG encoders pretrained with self-supervised learning are increasingly reused across tasks, institutions, and deployment contexts, often through model-as-a-service…

cs.LG2026

CARE-ECG: Causal Agent-based Reasoning for Explainable and Counterfactual ECG Interpretation

Elahe Khatibi, Ziyu Wang, Ankita Sharma +4

Large language models (LLMs) enable waveform-to-text ECG interpretation and interactive clinical questioning, yet most ECG-LLM systems still rely on weak signal-text alignment and…

cs.LG2026

Evaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in Healthcare

Nitish Nagesh, Elahe Khatibi, Thomas Hughes +3

Causal discovery in health data faces evaluation challenges when ground truth is unknown. We address this by collaborating with experts to construct proxy ground-truth graphs, esta…