Predicting positron emission tomography brain amyloid positivity using interpretable machine learning models with wearable sensor data and lifestyle factors

Predicting positron emission tomography brain amyloid positivity using interpretable machine learning models with wearable sensor data and lifestyle factors

Abstract Background Developing a screening method for identifying individuals at higher risk of elevated brain amyloid burden is important to reduce costs and burden to patients in clinical trials on Alzheimer’s disease or the clinical setting. We developed machine learning models using objectively...

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Bibliographic Details
Main Authors: Noriyuki Kimura, Tomoki Aota, Yasuhiro Aso, Kenichi Yabuuchi, Kotaro Sasaki, Teruaki Masuda, Atsuko Eguchi, Yoshitaka Maeda, Ken Aoshima, Etsuro Matsubara
Format: Article
Language:English
Published: BMC 2023-12-01
Series:Alzheimer’s Research & Therapy
Subjects:
Amyloid positivity
Lifestyle factors
Machine learning
Mild cognitive impairment
Wearable sensor
PiB-PET
Online Access:https://doi.org/10.1186/s13195-023-01363-x

Internet

https://doi.org/10.1186/s13195-023-01363-x

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