activity
20232026
most citedChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework

115 citations · 135 across the 16 of their papers we have counts for

collaborators

18 papers

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.CL2026

Personal Care Utility: Health as Everyday Infrastructure

Mahyar Abbasian, Elahe Khatibi, Saba A. Farahani +5

Healthcare is essential, expert, and episodic by design - built around the roughly one hour per year a person spends with a clinician. The 8,759 hours outside clinical settings, wh…

stat.AP2026

Event-Aligned Analysis of Multi-Rater Pain Assessments Using Continuous Wearable Physiology

Saba A. Farahani, Elahe Khatibi, Thomas D. Hughes +3

Pain is assessed differently by patients, nurses, and clinicians, yet most computational approaches assume a single ground-truth label - effectively ignoring who is doing the ratin…

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…

cs.CL2026

MedSpeak: A Knowledge Graph-Aided ASR Error Correction Framework for Spoken Medical QA

Yutong Song, Shiva Shrestha, Chenhan Lyu +5

Spoken question-answering (SQA) systems relying on automatic speech recognition (ASR) often struggle with accurately recognizing medical terminology. To this end, we propose MedSpe…

cs.CR2025

Linkage Attacks Expose Identity Risks in Public ECG Data Sharing

Ziyu Wang, Elahe Khatibi, Farshad Firouzi +3

The increasing availability of publicly shared electrocardiogram (ECG) data raises critical privacy concerns, as its biometric properties make individuals vulnerable to linkage att…

cs.CL2025

MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answering

Ziyu Wang, Elahe Khatibi, Amir M. Rahmani

Large language models (LLMs) have shown promise in medical question answering but often struggle with hallucinations and shallow reasoning, particularly in tasks requiring nuanced…

cs.CL2025★ 1 cited

CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation

Elahe Khatibi, Ziyu Wang, Amir M. Rahmani

Retrieval-Augmented Generation (RAG) has significantly enhanced large language models (LLMs) in knowledge-intensive tasks by incorporating external knowledge retrieval. However, ex…

eess.SP2025

TransECG: Leveraging Transformers for Explainable ECG Re-identification Risk Analysis

Ziyu Wang, Elahe Khatibi, Kianoosh Kazemi +4

Electrocardiogram (ECG) signals are widely shared across multiple clinical applications for diagnosis, health monitoring, and biometric authentication. While valuable for healthcar…

cs.LG2024★ 4 cited

ALCM: Autonomous LLM-Augmented Causal Discovery Framework

Elahe Khatibi, Mahyar Abbasian, Zhongqi Yang +2

To perform effective causal inference in high-dimensional datasets, initiating the process with causal discovery is imperative, wherein a causal graph is generated based on observa…

cs.LG2024★ 11 cited

Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecasting

Zhongqi Yang, Yuning Wang, Ken S. Yamashita +6

Emotional states, as indicators of affect, are pivotal to overall health, making their accurate prediction before onset crucial. Current studies are primarily centered on immediate…

cs.IR2024★ 115 cited

ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework

Zhongqi Yang, Elahe Khatibi, Nitish Nagesh +4

The profound impact of food on health necessitates advanced nutrition-oriented food recommendation services. Conventional methods often lack the crucial elements of personalization…

cs.CL2024★ 4 cited

Knowledge-Infused LLM-Powered Conversational Health Agent: A Case Study for Diabetes Patients

Mahyar Abbasian, Zhongqi Yang, Elahe Khatibi +5

Effective diabetes management is crucial for maintaining health in diabetic patients. Large Language Models (LLMs) have opened new avenues for diabetes management, facilitating the…

cs.CL2023

Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI

Mahyar Abbasian, Elahe Khatibi, Iman Azimi +11

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process.…