This article requires a subscription to view the full text. If you have a subscription you may use the login form below to view the article. Access to this article can also be purchased.
There is an urgent need to develop targeted and individualized preventive approaches for those who are likely to develop Alzheimer disease (AD). Machine learning is a field of computer science that deciphers complex relationships and patterns across a set of variables. It is emerging as a research tool that has potential for future use to screen populations and aid clinical decision-making. Whereas research into understanding the why and how of neurodegeneration, specifically in AD, will continue to take time to identify meaningful treatment targets, developing statistical and machine learning models to recognize high-risk individuals who require personalized treatment targeting before symptoms appear is an attractive line of research.
Footnotes
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 article.
See page 1007
- © 2022 American Academy of Neurology
AAN Members
We have changed the login procedure to improve access between AAN.com and the Neurology journals. If you are experiencing issues, please log out of AAN.com and clear history and cookies. (For instructions by browser, please click the instruction pages below). After clearing, choose preferred Journal and select login for AAN Members. You will be redirected to a login page where you can log in with your AAN ID number and password. When you are returned to the Journal, your name should appear at the top right of the page.
AAN Non-Member Subscribers
Purchase access
Letters: Rapid online correspondence
REQUIREMENTS
You must ensure that your Disclosures have been updated within the previous six months. Please go to our Submission Site to add or update your Disclosure information.
Your co-authors must send a completed Publishing Agreement Form to Neurology Staff (not necessary for the lead/corresponding author as the form below will suffice) before you upload your comment.
If you are responding to a comment that was written about an article you originally authored:
You (and co-authors) do not need to fill out forms or check disclosures as author forms are still valid
and apply to letter.
Submission specifications:
- Submissions must be < 200 words with < 5 references. Reference 1 must be the article on which you are commenting.
- Submissions should not have more than 5 authors. (Exception: original author replies can include all original authors of the article)
- Submit only on articles published within 6 months of issue date.
- Do not be redundant. Read any comments already posted on the article prior to submission.
- Submitted comments are subject to editing and editor review prior to posting.
