3 citations · 9 across the 13 of their papers we have counts for
14 papers
OpenMTB-Audit: Exposing Over-Refusal and Clinical Expert Perspectives in LLM-Based Molecular Tumor Board Safety Evaluation
Negin Ashrafi, Jia Luo, Stacey M. Frumm +1
Molecular tumor boards integrate genomic findings, clinical context, and therapeutic evidence to support precision oncology. As AI enters this workflow, a key safety challenge is d…
Auditing Construct Overlap in Explainable Machine Learning: Evidence from Burnout-Depression Prediction Across Student Cohorts
Alireza Dehghan, Negin Ashrafi
Explainable machine learning (XML) pipelines applied to composite mental health outcomes can produce apparently-robust, cross-population-stable risk hierarchies that are largely ar…
TimingLLM: A Two-Stage Retrieval-Augmented Framework for Pre-Synthesis Timing Prediction from Verilog
Armin Abdollahi, Negin Ashrafi, Mehdi Kamal +1
Early, tool-free prediction of post-synthesis timing remains a key obstacle to rapid RTL iteration. We introduce TimingLLM, a two-stage retrieval-augmented LLM pipeline that estima…
HDLFORGE: A Two-Stage Multi-Agent Framework for Efficient Verilog Code Generation with Adaptive Model Escalation
Armin Abdollahi, Saeid Shokoufa, Negin Ashrafi +2
We present HDLFORGE, a two-stage multi-agent framework for automated Verilog generation that optimizes the trade-off between generation speed and accuracy. The system uses a compac…
Aligning Language Models with Clinical Expertise: DPO for Heart Failure Nursing Documentation in Critical Care
Junyi Fan, Li Sun, Negin Ashrafi +2
Nursing documentation in intensive care units (ICUs) provides essential clinical intelligence but often suffers from inconsistent terminology, informal styles, and lack of standard…
LLM Encoder vs. Decoder: Robust Detection of Chinese AI-Generated Text with LoRA
Houji Jin, Negin Ashrafi, Armin Abdollahi +5
The rapid growth of large language models (LLMs) has heightened the demand for accurate detection of AI-generated text, particularly in languages like Chinese, where subtle linguis…
Optimizing Urban Mobility Through Complex Network Analysis and Big Data from Smart Cards
Li Sun, Negin Ashrafi, Maryam Pishgar
This study investigates the network characteristics of high-frequency (HF) and low-frequency (LF) travelers in urban public transport systems by analyzing 20 million smart card rec…
A Novel Multi-Task Teacher-Student Architecture with Self-Supervised Pretraining for 48-Hour Vasoactive-Inotropic Trend Analysis in Sepsis Mortality Prediction
Houji Jin, Negin Ashrafi, Kamiar Alaei +3
Sepsis is a major cause of ICU mortality, where early recognition and effective interventions are essential for improving patient outcomes. However, the vasoactive-inotropic score…
Machine Learning-Based Prediction of ICU Readmissions in Intracerebral Hemorrhage Patients: Insights from the MIMIC Databases
Shuheng Chen, Junyi Fan, Armin Abdollahi +4
Intracerebral hemorrhage (ICH) is a life-risking condition characterized by bleeding within the brain parenchyma. ICU readmission in ICH patients is a critical outcome, reflecting…
Optimizing Mortality Prediction for ICU Heart Failure Patients: Leveraging XGBoost and Advanced Machine Learning with the MIMIC-III Database
Negin Ashrafi, Armin Abdollahi, Jiahong Zhang +1
Heart failure affects millions of people worldwide, significantly reducing quality of life and leading to high mortality rates. Despite extensive research, the relationship between…
Enhanced Prediction of Ventilator-Associated Pneumonia in Patients with Traumatic Brain Injury Using Advanced Machine Learning Techniques
Negin Ashrafi, Armin Abdollahi, Maryam Pishgar
Background: Ventilator-associated pneumonia (VAP) in traumatic brain injury (TBI) patients poses a significant mortality risk and imposes a considerable financial burden on patient…
Data-Driven Machine Learning Approaches for Predicting In-Hospital Sepsis Mortality
Arseniy Shumilov, Yueting Zhu, Negin Ashrafi +4
Sepsis is a severe condition responsible for many deaths in the United States and worldwide, making accurate prediction of outcomes crucial for timely and effective treatment. Prev…
Effect of a Process Mining based Pre-processing Step in Prediction of the Critical Health Outcomes
Negin Ashrafi, Armin Abdollahi, Greg Placencia +1
Predicting critical health outcomes such as patient mortality and hospital readmission is essential for improving survivability. However, healthcare datasets have many concurrences…
Advanced Predictive Modeling for Enhanced Mortality Prediction in ICU Stroke Patients Using Clinical Data
Armin Abdollahi, Negin Ashrafi, Maryam Pishgar
Background: Stroke is second-leading cause of disability and death among adults. Approximately 17 million people suffer from a stroke annually, with about 85% being ischemic stroke…