7 citations · 8 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
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…
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…
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…
Embedding-Driven Diversity Sampling to Improve Few-Shot Synthetic Data Generation
Ivan Lopez, Fateme Nateghi Haredasht, Kaitlin Caoili +2
Accurate classification of clinical text often requires fine-tuning pre-trained language models, a process that is costly and time-consuming due to the need for high-quality data a…
Quantization-aware Matrix Factorization for Low Bit Rate Image Compression
Pooya Ashtari, Pourya Behmandpoor, Fateme Nateghi Haredasht +3
Lossy image compression is essential for efficient transmission and storage. Traditional compression methods mainly rely on discrete cosine transform (DCT) or singular value decomp…
Supervised Fuzzy Partitioning
Pooya Ashtari, Fateme Nateghi Haredasht, Hamid Beigy
Centroid-based methods including k-means and fuzzy c-means are known as effective and easy-to-implement approaches to clustering purposes in many applications. However, these algor…