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

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 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 natural language processing modified rankin scale machine learning

Published: Oct. 2, 2025. Version: 1.0.0

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 ehr epilepsy

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

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

Software Credentialed Access

Epileptiform activity and outcomes in toxic-metabolic encephalopathy

Paul M. Chen, Saskia S. Schuurmans Stekhoven, Hiba Haider, Jin Jing, Wendong Ge, Eric Rosenthal, M. Brandon Westover, Sahar F. Zafar

De-identified data and reproduction code for a retrospective cEEG study of ictal-interictal continuum patterns and outcomes in 121 patients with toxic-metabolic encephalopathy (MGH, 2012-2017).

Published: July 10, 2026. Version: 1.0.0

Software Credentialed Access

2HELPS2B: an EEG-based risk score for seizure probability in hospitalized patients

Aaron F Struck, Berk Ustun, Andres A. Rodriguez Ruiz, Jong Woo Lee, Suzette M. LaRoche, Lawrence J. Hirsch, emily gilmore, Jan Vlachy, Hiba Haider, Cynthia Rudin, M. Brandon Westover

MATLAB code and de-identified CCEMRC data behind the 2HELPS2B seizure-risk score: seizure probability by ictal-interictal-continuum EEG pattern (5,742 continuous-EEG records).

Published: July 9, 2026. Version: 1.0.0

Database Credentialed Access

The Neurotech EEG Dataset

Keith Morgan, Charles Pickering, Matthew Goodwin, Han Wu, Manohar Ghanta, Aditya Gupta, Daniel Goldenholz, M. Brandon Westover

A large clinical scalp EEG corpus: 23,607 recordings from 4,914 patients (212,186 hours) acquired in patients' homes and hospitals on Natus/Xltek hardware, in BIDS-EEG format with technician annotations and de-identified clinical metadata.

Published: July 7, 2026. Version: 1.0