Exploring Automated Machine Learning for Cognitive Outcome Prediction from Multimodal Brain Imaging using STREAMLINE.

Autor:	Wang X; University of Pennsylvania, Philadelphia, PA., Feng Y; University of Pennsylvania, Philadelphia, PA., Tong B; University of Pennsylvania, Philadelphia, PA., Bao J; University of Pennsylvania, Philadelphia, PA., Ritchie MD; University of Pennsylvania, Philadelphia, PA., Saykin AJ; Indiana University, Indianapolis, IN., Moore JH; Cedars-Sinai Medical Center, West Hollywood, CA., Urbanowicz R; Cedars-Sinai Medical Center, West Hollywood, CA., Shen L; University of Pennsylvania, Philadelphia, PA.
Jazyk:	angličtina
Zdroj:	AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science [AMIA Jt Summits Transl Sci Proc] 2023 Jun 16; Vol. 2023, pp. 544-553. Date of Electronic Publication: 2023 Jun 16 (Print Publication: 2023).
Abstrakt:	STREAMLINE is a simple, transparent, end-to-end automated machine learning (AutoML) pipeline for easily conducting rigorous machine learning (ML) modeling and analysis. The initial version is limited to binary classification. In this work, we extend STREAMLINE through implementing multiple regression-based ML models, including linear regression, elastic net, group lasso, and L21 norm. We demonstrate the effectiveness of the regression version of STREAMLINE by applying it to the prediction of Alzheimer's disease (AD) cognitive outcomes using multimodal brain imaging data. Our empirical results demonstrate the feasibility and effectiveness of the newly expanded STREAMLINE as an AutoML pipeline for evaluating AD regression models, and for discovering multimodal imaging biomarkers. (©2023 AMIA - All rights reserved.)
Databáze:	MEDLINE
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