Putting the “Big” in Big Data
Learning to Be Just as (Un)certain as a Clinician at EEG
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The deployment of machine learning to augment clinical judgment is an exciting avenue in the effort to improve patient care. In basic terms, machine learning can be thought of as an algorithm that learns how to distinguish patterns within data, and it can learn better with larger amounts of data. Distinguishing patterns is at the heart of many aspects of clinical care. EEG interpretation is a prime example: a trained, laborious task that might benefit from algorithms. The challenge, however, is to build an algorithm that is reliable enough to be useful and trustworthy in standard clinical care.
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Go to Neurology.org/N for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the editorial.
See page 806
See page 808
- Received January 24, 2023.
- Accepted in final form February 9, 2023.
- © 2023 American Academy of Neurology
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