Improving Clinician Performance in Classifying EEG Patterns on the Ictal-Interictal Injury Continuum Using Interpretable Machine Learning.
Alina Jade BarnettZhicheng GuoJin JingWendong GePeter W KaplanWan Yee KongIoannis KarakisAline HerlopianLakshman Arcot JayagopalOlga TaraschenkoOlga SelioutskiGamaleldin OsmanDaniel M GoldenholzCynthia D RudinMichael Brandon WestoverPublished in: NEJM AI (2024)
Users showed significant pattern classification accuracy improvement with the assistance of this interpretable deep-learning model. The interpretable design facilitates effective human-AI collaboration; this system may improve diagnosis and patient care in clinical settings. The model may also provide a better understanding of how EEG patterns relate to each other along the ictal-interictal injury continuum. (Funded by the National Science Foundation, National Institutes of Health, and others.).
Keyphrases
- deep learning
- machine learning
- artificial intelligence
- temporal lobe epilepsy
- public health
- functional connectivity
- endothelial cells
- resting state
- quality improvement
- working memory
- healthcare
- mental health
- convolutional neural network
- big data
- risk assessment
- induced pluripotent stem cells
- health information
- human health