Search Results for author:
Found 7 papers, 2 papers with code
Interpretable Machine Learning System to EEG Patterns on the Ictal-Interictal-Injury Continuum
To address these challenges, we propose a novel interpretable deep learning model that not only predicts the presence of harmful brainwave patterns but also provides high-quality case-based explanations of its decisions.
Effects of Epileptiform Activity on Discharge Outcome in Critically Ill Patients
Having a maximum EA burden greater than 75% when untreated had a 22% increased chance of a poor outcome (severe disability or death), and mild but long-lasting EA increased the risk of a poor outcome by 14%.
NeuraHealth: An Automated Screening Pipeline to Detect Undiagnosed Cognitive Impairment in Electronic Health Records with Deep Learning and Natural Language Processing
Dementia related cognitive impairment (CI) is a neurodegenerative disorder, affecting over 55 million people worldwide and growing rapidly at the rate of one new case every 3 seconds.
Using Deep Learning to Identify Patients with Cognitive Impairment in Electronic Health Records
Automated mining of these notes presents an opportunity to label patients with cognitive impairment in EHR data.
Natural Language Processing to Detect Cognitive Concerns in Electronic Health Records Using Deep Learning
Dementia is under-recognized in the community, under-diagnosed by healthcare professionals, and under-coded in claims data.
