115 citations · 157 across the 19 of their papers we have counts for
20 papers
A knowledge-guided agentic framework for mitigating patient-context ambiguity in health queries
Mahyar Abbasian, Saba A. Farahani, Arshia Ilaty +3
Patients often submit short, underspecified queries to healthcare chatbots that lack the patient-specific information needed to determine an appropriate response. Although these qu…
When Clean Signals Are Not Enough: Detecting Structural Ambiguity for Safe Wearable Stress Classification
Saba A. Farahani, Hung Cao, Amir M. Rahmani
Wearable stress classifiers can achieve strong average performance while failing completely for a particular individual. On WESAD, a Random Forest reaches 93.0% mean accuracy yet y…
From Sounds to Scenes: A Benchmark for Evaluating Context-Aware Auditory Scene Understanding in Large Audio Language Models
Pengfei Zhang, Hoang H Nguyen, Kazi Shaharair Sharif +6
Recent Large Audio Language Models (LALMs) have achieved remarkable progress in audio perceptual tasks across individual acoustic layers, including speech, sound, and music. Howeve…
T2D-Bench: Evidence-Gated Evaluation of LLM Outputs for Type 2 Diabetes Using a Multi-Layer Clinical-Lifestyle Knowledge Graph
Saba A. Farahani, Hung Cao, Ramesh Jain +1
Large language models (LLMs) can produce clinically fluent recommendations for type 2 diabetes while failing to satisfy guideline constraints or explicitly justify lifestyle-relate…
PSyGenTAB: A Privacy-Preserving Framework for Synthetic Clinical Tabular Data Generation via Constrained Optimization
Arshia Ilaty, Hossein Shirazi, Manasi Chitale +5
The development of medical AI is constrained by limited access to high-quality clinical data due to institutional silos and strict privacy regulations such as HIPAA and GDPR. Synth…
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…
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…
Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets
Nitish Nagesh, Pengbao Zhou, Atchuth Naveen Chilaparasetti +8
Synthetic data is widely used in healthcare to create datasets that preserve statistical properties of real data without exposing sensitive patient information. Generating and eval…
Query-Conditioned Graph Retrieval for Contextualized LLM Reasoning in Personalized Wearable Data
Zhenyu Lu, Mahyar Abbasian, Amir M. Rahmani
Large language models (LLMs) are increasingly applied to analyzing wearable sensing data, which are long-term, multimodal, and highly personalized. A key challenge is context selec…
An LLM-Powered Agent for Physiological Data Analysis: A Case Study on PPG-based Heart Rate Estimation
Mohammad Feli, Iman Azimi, Pasi Liljeberg +1
Large language models (LLMs) are revolutionizing healthcare by improving diagnosis, patient care, and decision support through interactive communication. More recently, they have b…
Accuracy and Consistency of LLMs in the Registered Dietitian Exam: The Impact of Prompt Engineering and Knowledge Retrieval
Iman Azimi, Mohan Qi, Li Wang +2
Large language models (LLMs) are fundamentally transforming human-facing applications in the health and well-being domains: boosting patient engagement, accelerating clinical decis…
Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis
Ajan Subramanian, Zhongqi Yang, Iman Azimi +1
Health monitoring systems have revolutionized modern healthcare by enabling the continuous capture of physiological and behavioral data, essential for preventive measures and early…
Empathy Through Multimodality in Conversational Interfaces
Mahyar Abbasian, Iman Azimi, Mohammad Feli +2
Agents represent one of the most emerging applications of Large Language Models (LLMs) and Generative AI, with their effectiveness hinging on multimodal capabilities to navigate co…
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…
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…
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…
Food Recommendation as Language Processing (F-RLP): A Personalized and Contextual Paradigm
Ali Rostami, Ramesh Jain, Amir M. Rahmani
State-of-the-art rule-based and classification-based food recommendation systems face significant challenges in becoming practical and useful. This difficulty arises primarily beca…
Conversational Health Agents: A Personalized LLM-Powered Agent Framework
Mahyar Abbasian, Iman Azimi, Amir M. Rahmani +1
Conversational Health Agents (CHAs) are interactive systems that provide healthcare services, such as assistance and diagnosis. Current CHAs, especially those utilizing Large Langu…
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.…
Controlling the Latent Space of GANs through Reinforcement Learning: A Case Study on Task-based Image-to-Image Translation
Mahyar Abbasian, Taha Rajabzadeh, Ahmadreza Moradipari +3
Generative Adversarial Networks (GAN) have emerged as a formidable AI tool to generate realistic outputs based on training datasets. However, the challenge of exerting control over…