Resources


Model Credentialed Access

Automated Prediction of Glasgow Coma Scale Scores from Unstructured Electronic Health Records: a Natural Language Processing Approach

Marta Fernandes, Niels Turley, Haoqi Sun, Shibani Mukerji, Lidia M. V. R. Moura, M Brandon Westover, Sahar Zafar

Prediction of Glasgow Coma Scale scores from Unstructured Electronic Health Records using NLP

glasgow coma scale natural language processing ordinal regression electronic health records clinical notes

Published: April 17, 2026. Version: 1.0.0

Model Credentialed Access

Automated extraction of post-stroke functional outcomes from unstructured electronic health records

Marta Fernandes, Kaileigh Gallagher, Niels Turley, Aditya Gupta, M Brandon Westover, Aneesh Singhal, Sahar Zafar

This project aims to automatically extract mRS scores for a post-stroke patient population from unstructured electronic health records using natural language processing

stroke machine learning natural language processing modified rankin scale

Published: Oct. 2, 2025. Version: 1.0.0

Database Open Access

NIDX: A Machine Learning Approach for Identifying People with Neuroinfectious Diseases in Electronic Health Records

Arjun Singh, Shadi Sartipi, Haoqi Sun, Niels Turley, Sahar Zafar, Sudeshna Das, Marta Fernandes, M Brandon Westover, Shibani Mukerji

A machine learning approach to accurately identify neuroinfectious diseases from clinical notes.

natural language processing electronic health records ehr phenotyping neuroinfectious diseases

Published: May 31, 2025. Version: 1.0

Model Credentialed Access

Automated extraction of stroke severity from unstructured electronic health records using natural language processing

Marta Fernandes, M Brandon Westover, Aneesh Singhal, Sahar Zafar

This project automatically extracts NIHSS scores from unstructured electronic health records using natural language processing

nihss nlp stroke

Published: Oct. 2, 2025. Version: 1.0.0

Database Credentialed Access

Identification of patients with epilepsy using automated electronic health records phenotyping - Data and Code

Marta Fernandes, Sahar Zafar, M Brandon Westover

Code and data for identifying patients with epilepsy using automated electronic health records.

nlp epilepsy ehr

Published: June 5, 2025. Version: 1.0

Database Credentialed Access

Automated phenotyping of mild cognitive impairment and dementias using electronic health records - Data and Code

Ruoqi Wei, Robert Thomas, M Brandon Westover, Haoqi Sun

Data and Code to reproduce results in "Automated phenotyping of mild cognitive impairment and dementias using electronic health records"

nlp ad mci

Published: June 5, 2025. Version: 1.0

Database Credentialed Access

Narcolepsy Risk Estimation from Clinical Notes

Niels Turley, Haoqi Sun, M Brandon Westover

Dataset and code for developing and validating machine learning models to phenotype narcolepsy type 1 (NT1) and narcolepsy type 2/idiopathic hypersomnia (NT2/IH) from multi-site electronic health record data, including cross-sectional classification

Published: March 2, 2026. Version: 1.0

Database Credentialed Access

PRediction Of Disease PHEnoTypes (PROPHET)

Niels Turley, Marta Fernandes, Shadi Sartipi, Han Wu, Alice Lam, Lydia Petersen, Catherine Clive, Daniel Sumsion, Ruoqi Wei, Bram Overmeer, Jaden Searle, Gregory Hooke, Spencer Boris, Wan-Yee Kong, Arjun Singh, Marjan Sarami, Alihan Yaramis, Imad Akbar, Rebecca Milde, Jet Veltink, Elijah Davis, Aditya Gupta, Manohar Ghanta, Aidan McDonald Wojciechowski, Shibani Mukerji, Haoqi Sun, M Brandon Westover, Sahar Zafar

Multicenter expert-annotated EHR dataset and NLP phenotyping framework for 17 neurological conditions spanning diagnoses, severity scales, and outcomes across six U.S. health systems.

Published: March 31, 2026. Version: 1.0

Model Open Access

Automated phenotyping of mild cognitive impairment and Alzheimer's disease and related dementias using electronic health records

Ruoqi Wei, Niels Turley, Aditya Gupta, Manohar Ghanta, Robert Thomas, Sahar Zafar, Haoqi Sun, M Brandon Westover

a MCI/ADRD EHR phenotyping model trained with python sklearn pipeline, injoblib format.

Published: Sept. 25, 2025. Version: 1.1