activity
20192026
most citedObesity Prediction with EHR Data: A deep learning approach with interpretable elements

101 citations · 161 across the 22 of their papers we have counts for

collaborators
Showing 2024Show all

6 papers · 1 filter

cs.CL2024★ 1 cited

Enabling Scalable Evaluation of Bias Patterns in Medical LLMs

Hamed Fayyaz, Raphael Poulain, Rahmatollah Beheshti

Large language models (LLMs) have shown impressive potential in helping with numerous medical challenges. Deploying LLMs in high-stakes applications such as medicine, however, brin…

cs.CL2024★ 1 cited

Aligning (Medical) LLMs for (Counterfactual) Fairness

Raphael Poulain, Hamed Fayyaz, Rahmatollah Beheshti

Large Language Models (LLMs) have emerged as promising solutions for a variety of medical and clinical decision support applications. However, LLMs are often subject to different t…

cs.LG2024★ 2 cited

Fairness-Optimized Synthetic EHR Generation for Arbitrary Downstream Predictive Tasks

Mirza Farhan Bin Tarek, Raphael Poulain, Rahmatollah Beheshti

Among various aspects of ensuring the responsible design of AI tools for healthcare applications, addressing fairness concerns has been a key focus area. Specifically, given the wi…

cs.CL2024★ 7 cited

Bias patterns in the application of LLMs for clinical decision support: A comprehensive study

Raphael Poulain, Hamed Fayyaz, Rahmatollah Beheshti

Large Language Models (LLMs) have emerged as powerful candidates to inform clinical decision-making processes. While these models play an increasingly prominent role in shaping the…

cs.LG2024

HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks

Fahmida Liza Piya, Mehak Gupta, Rahmatollah Beheshti

While electronic health records (EHRs) are widely used across various applications in healthcare, most applications use the EHRs in their raw (tabular) format. Relying on raw or si…

eess.SP2024

Multimodal Sleep Apnea Detection with Missing or Noisy Modalities

Hamed Fayyaz, Abigail Strang, Niharika S. D'Souza +1

Polysomnography (PSG) is a type of sleep study that records multimodal physiological signals and is widely used for purposes such as sleep staging and respiratory event detection.…