Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis. - Fatemeh Amrollahi; Supreeth P. Shashikumar; Fereshteh Razmi; Shamim Nemati (AMIA, 2020)

AIDEx - An Open-source Platform for Real-Time Forecasting Sepsis and A Case Study on Taking ML Algorithms to Production. - Fatemeh Amrollahi; Supreeth Prajwal Shashikumar; Pradeeban Kathiravelu; Ashish Sharma 001; Shamim Nemati (EMBC, 2020)

Inclusion of social determinants of health improves sepsis readmission prediction models. - Fatemeh Amrollahi; Supreeth Prajwal Shashikumar; Angela Meier; Lucila Ohno-Machado; Shamim Nemati; Gabriel Wardi (J. Am. Medical Informatics Assoc., 2022)

Predicting Hospital Readmission among Patients with Sepsis Using Clinical and Wearable Data. - Fatemeh Amrollahi; Supreeth Prajwal Shashikumar; Aaron Boussina; Haben Yhdego; Arshia Nayebnazar; Nathan Yung; Gabriel Wardi; Shamim Nemati (EMBC, 2023)

Development & Deployment of a Real-time Healthcare Predictive Analytics Platform. - Aaron Boussina; Supreeth P. Shashikumar; Fatemeh Amrollahi; Hayden Pour; Michael A. Hogarth; Shamim Nemati (EMBC, 2023)

Unsupervised Detection and Correction of Model Calibration Shift at Test-Time. - Supreeth P. Shashikumar; Fatemeh Amrollahi; Shamim Nemati (EMBC, 2023)

Antibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records. - Fateme Nateghi Haredasht; Fatemeh Amrollahi; Manoj Maddali; Nicholas Marshall; Stephen P. Ma; Lauren N. Cooper; Richard J. Medford; Sanjat Kanjilal; Niaz Banaei; Stanley Deresinski; Mary K. Goldstein; Steven M. Asch; et al. (CoRR, 2025)

A Multi-Phase Analysis of Blood Culture Stewardship: Machine Learning Prediction, Expert Recommendation Assessment, and LLM Automation. - Fatemeh Amrollahi; Nicholas Marshall; Fateme Nateghi Haredasht; Kameron C. Black; Aydin Zahedivash; Manoj Maddali; Stephen P. Ma; Amy Chang; Stanley Deresinski; Mary K. Goldstein; Steven M. Asch; Niaz Banaei; et al. (CoRR, 2025)