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Found 5 papers, 1 papers with code
Towards Interpretable Seizure Detection Using Wearables
Seizure detection using machine learning is a critical problem for the timely intervention and management of epilepsy.
Performance and utility trade-off in interpretable sleep staging
Clinical sleep staging is an arduous process requiring manual annotation for each 30s of sleep using physiological signals such as electroencephalogram (EEG).
CCS Explorer: Relevance Prediction, Extractive Summarization, and Named Entity Recognition from Clinical Cohort Studies
CCS Explorer then extracts and aggregates all relevant information from articles on PubMed based on the results of an automatically generated query produced on the back-end.
SERF: Interpretable Sleep Staging using Embeddings, Rules, and Features
SERF surpasses the current state-of-the-art for interpretable sleep staging by 2%.
SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules
In this study, we propose Sleep staging via Prototypes from Expert Rules (SLEEPER), which combines deep learning models with expert defined rules using a prototype learning framework to generate simple interpretable models.
Ranked #1 on
Automatic Sleep Stage Classification
on ISRUC-Sleep
