Improving Clinician Performance in Classifying EEG Patterns on the Ictal–Interictal Injury Continuum Using Interpretable Machine Learning is a research paper published in NEJM AI (2024). On theSindex it has a DataRank of 0. It has been cited 29 times.
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NINDS NIH HHS
Grant: K23 NS124656
NIGMS NIH HHS
Grant: P20 GM130447
NINDS NIH HHS
Grant: R01 NS107291
NCATS NIH HHS
Grant: UL1 TR001863
NIA NIH HHS
Grant: R01 AG073410
NINDS NIH HHS
Grant: R01 NS102190
NINDS NIH HHS
Grant: R01 NS102574
NINDS NIH HHS
Grant: RF1 NS120947
NHLBI NIH HHS
Grant: R01 HL161253
NIA NIH HHS
Grant: RF1 AG064312
National Institutes of Health
Grant: 5R01NS102574-05
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National Science Foundation
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National Institutes of Health
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National Institutes of Health
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Comparative Safety of Seizure Prophylaxis within the Medicare Program
National Science Foundation
Grant: 2130250
EAGER: Creating an Unsupervised Interpretable Representation of the World Through Concept Disentanglement
National Institutes of Health
Grant: 1R01NS107291-01
Big Data and Deep Learning for the Interictal-Ictal-Injury Continuum
National Science Foundation
Grant: 2222336
FW-HTF-R: Interpretable Machine Learning for Human-Machine Collaboration in High Stakes Decisions in Mammography
National Institutes of Health
Grant: 1RF1AG064312-01
Integrative Motor Activity Biomarker for the Risk of Alzheimer's Risk
National Science Foundation
Grant: 2014431
SCH: INT: Collaborative Research: DeepSense: Interpretable Deep Learning for Zero-effort Phenotype Sensing and Its Application to Sleep Medicine
National Institutes of Health
Grant: 7R01NS102190-06
Investigation of Sleep
National Institutes of Health
Grant: 7RF1NS120947-02
Establishing a Brain Health Index
FWCI
6.95
Citation Percentile
1.0%
Citation Trend
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Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine Learning