Resources

De-identified data and a PK-PD + MALTS causal-inference pipeline reproducing the effect of untreated epileptiform-activity burden on poor discharge outcome in critically ill patients (Parikh et al., Lancet Digital Health 2023).

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).

An interpretable ordinal EEG-feature score (VE-ICANS) that grades the severity of immune effector cell-associated neurotoxicity syndrome (ICANS) after CAR-T therapy. Code + de-identified data.

A continuous, EEG-based index of encephalopathy severity (NESI) that maps RASS, GCS, CAM-S, and ICANS onto one physiological axis. Includes 10-minute EEG segments, MORGOTH features, trained models, derived tables, and the full analysis code.

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.

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

The Boston Children’s Hospital (BCH) Sleep Corpus comprises 15,695 fully annotated pediatric polysomnography (PSG) recordings collected between 2010 and 2024.

An expert-level deep learning model for detecting epileptiform discharges in EEG recordings at both event and whole-EEG levels.

Data and code for "Ordinal Sleep Depth: A Data-Driven Continuous Measurement of Sleep Depth"

Data and code to reproduce this paper: https://pmc.ncbi.nlm.nih.gov/articles/PMC9847346/