Resources

iOS-app-based EEG education and gamified learning data: 2,270 participants rated candidate interictal epileptiform discharges (IEDs) on the DiagnosUs app, with 13,262 EEG candidates, survey responses, and learning curves.

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

Initial version

Data and code for a bi-institutional randomized controlled educational pilot trial (n=21 junior neurology residents) evaluating a web- and app-based training program for identifying interictal epileptiform discharges (IEDs) on EEG.

Dataset and code for developing and validating the EICANS score, a machine learning–derived EEG metric for automated detection and grading of immune effector cell-associated neurotoxicity syndrome (ICANS) in patients receiving CAR-T cell therapy.

BIND Database 1: Neuroimaging Data (MRI, CT, PET, SPECT) that can be paired with EEG and PSG (found in the Harvard EEG Database https://bdsp.io/content/harvard-eeg-db/4.1/). LLMs helped categorize pathology.

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

De-identified polysomnography dataset and analysis code from Ye et al. 2020 (JAMA Netw Open): sleep-EEG-based Brain Age Index (BAI) computed across 9,834 MGH Sleep Lab PSGs and tested as a biomarker for dementia and MCI versus healthy controls.

Code and curated polysomnography dataset (10,784 PSGs from 8,044 patients, MGB Sleep Lab 2009-2019) used in Ye et al. 2023 (Sleep) to detect dementia and MCI from sleep EEG features.

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