collaborators

8 papers

cs.CL2025

Asking the Right Questions: Benchmarking Large Language Models in the Development of Clinical Consultation Templates

Liam G. McCoy, Fateme Nateghi Haredasht, Kanav Chopra +15

This study evaluates the capacity of large language models (LLMs) to generate structured clinical consultation templates for electronic consultation. Using 145 expert-crafted templ…

cs.CL2025

Retrieval-Augmented Guardrails for AI-Drafted Patient-Portal Messages: Error Taxonomy Construction and Large-Scale Evaluation

Wenyuan Chen, Fateme Nateghi Haredasht, Kameron C. Black +4

Asynchronous patient-clinician messaging via EHR portals is a growing source of clinician workload, prompting interest in large language models (LLMs) to assist with draft response…

cs.CL2025

MedFactEval and MedAgentBrief: A Framework and Workflow for Generating and Evaluating Factual Clinical Summaries

François Grolleau, Emily Alsentzer, Timothy Keyes +17

Evaluating factual accuracy in Large Language Model (LLM)-generated clinical text is a critical barrier to adoption, as expert review is unscalable for the continuous quality assur…

q-bio.QM2025

Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs

Fateme Nateghi Haredasht, Fatemeh Amrollahi, Manoj Maddali +13

The Antibiotic Resistance Microbiology Dataset (ARMD) is a de-identified resource derived from electronic health records (EHR) that facilitates research in antimicrobial resistance…

cs.LG2025

A Multi-Phase Analysis of Blood Culture Stewardship: Machine Learning Prediction, Expert Recommendation Assessment, and LLM Automation

Fatemeh Amrollahi, Nicholas Marshall, Fateme Nateghi Haredasht +10

Blood cultures are often over ordered without clear justification, straining healthcare resources and contributing to inappropriate antibiotic use pressures worsened by the global…

eess.IV2025

Deconver: A Deconvolutional Network for Medical Image Segmentation

Pooya Ashtari, Shahryar Noei, Fateme Nateghi Haredasht +4

While convolutional neural networks (CNNs) and vision transformers (ViTs) have advanced medical image segmentation, they face inherent limitations such as local receptive fields in…