3 citations · 11 across the 24 of their papers we have counts for
24 papers
Which Voices Move Markets? Speaker Identity and the Cross-Section of Post-Earnings Returns
Karmanpartap Singh Sidhu, Junyi Fan, Maryam Pishgar
We utilize FinBERT, a domain-specific transformer model, to parse 6.5 million sentences from 16,428 S&P 500 quarterly earnings call transcripts (2015-2025) and demonstrate that pos…
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…
Interpretable Machine Learning Model for Early Prediction of Acute Kidney Injury in Critically Ill Patients with Cirrhosis: A Retrospective Study
Li Sun, Shuheng Chen, Junyi Fan +5
Background: Cirrhosis is a progressive liver disease with high mortality and frequent complications, notably acute kidney injury (AKI), which occurs in up to 50% of hospitalized pa…
Prediction of Significant Creatinine Elevation in First ICU Stays with Vancomycin Use: A retrospective study through Catboost
Junyi Fan, Li Sun, Shuheng Chen +6
Background: Vancomycin, a key antibiotic for severe Gram-positive infections in ICUs, poses a high nephrotoxicity risk. Early prediction of kidney injury in critically ill patients…
Early Mortality Prediction in ICU Patients with Hypertensive Kidney Disease Using Interpretable Machine Learning
Yong Si, Junyi Fan, Li Sun +6
Background: Hypertensive kidney disease (HKD) patients in intensive care units (ICUs) face high short-term mortality, but tailored risk prediction tools are lacking. Early identifi…
Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach
Li Sun, Shuheng Chen, Yong Si +5
Background: Patients with both diabetes mellitus (DM) and atrial fibrillation (AF) face elevated mortality in intensive care units (ICUs), yet models targeting this high-risk group…
Predicting Short-Term Mortality in Elderly ICU Patients with Diabetes and Heart Failure: A Distributional Inference Framework
Junyi Fan, Shuheng Chen, Li Sun +5
Elderly ICU patients with coexisting diabetes mellitus and heart failure experience markedly elevated short-term mortality, yet few predictive models are tailored to this high-risk…
Interpretable Machine Learning Model for Early Prediction of 30-Day Mortality in ICU Patients With Coexisting Hypertension and Atrial Fibrillation: A Retrospective Cohort Study
Shuheng Chen, Yong Si, Junyi Fan +5
Hypertension and atrial fibrillation (AF) often coexist in critically ill patients, significantly increasing mortality rates in the ICU. Early identification of high-risk individua…
Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Tinghuan Li, Shuheng Chen, Junyi Fan +4
Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalization, elevated healthcare costs,…
Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia
Junyi Fan, Shuheng Chen, Li Sun +5
Aplastic anemia is a rare, life-threatening hematologic disorder characterized by pancytopenia and bone marrow failure. ICU admission in these patients often signals critical compl…
Predicting ICU Readmission in Acute Pancreatitis Patients Using a Machine Learning-Based Model with Enhanced Clinical Interpretability
Shuheng Chen, Yong Si, Junyi Fan +5
Acute pancreatitis (AP) is a common and potentially life-threatening gastrointestinal disease that imposes a significant burden on healthcare systems. ICU readmissions among AP pat…
Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients
Yong Si, Junyi Fan, Li Sun +5
Traumatic Brain Injury (TBI) is a major contributor to mortality among older adults, with geriatric patients facing disproportionately high risk due to age-related physiological vu…
Machine Learning-Based Model for Postoperative Stroke Prediction in Coronary Artery Disease
Haonan Pan, Shuheng Chen, Elham Pishgar +3
Coronary artery disease remains one of the leading causes of mortality globally. Despite advances in revascularization treatments like PCI and CABG, postoperative stroke is inevita…
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 Mortality in Sepsis-Associated Acute Kidney Injury Patients Using MIMIC-IV Database with Validation from eICU Database
Shuheng Chen, Junyi Fan, Elham Pishgar +3
Background: Sepsis-Associated Acute Kidney Injury (SA-AKI) leads to high mortality in intensive care. This study develops machine learning models using the Medical Information Mart…
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…
Utilizing Machine Learning Models to Predict Acute Kidney Injury in Septic Patients from MIMIC-III Database
Aleyeh Roknaldin, Zehao Zhang, Jiayuan Xu +2
Sepsis is a severe condition that causes the body to respond incorrectly to an infection. This reaction can subsequently cause organ failure, a major one being acute kidney injury…
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…