Matches in SemOpenAlex for { <https://semopenalex.org/work/W4320495280> ?p ?o ?g. }
- W4320495280 abstract "The Parkinson’s Progression Markers Initiative (PPMI) has collected more than a decade’s worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and ‘omics’ biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort." @default.
- W4320495280 created "2023-02-14" @default.
- W4320495280 creator A5007619706 @default.
- W4320495280 creator A5024101307 @default.
- W4320495280 creator A5039263043 @default.
- W4320495280 creator A5048081504 @default.
- W4320495280 creator A5056430594 @default.
- W4320495280 creator A5083074489 @default.
- W4320495280 date "2023-02-13" @default.
- W4320495280 modified "2023-09-25" @default.
- W4320495280 title "Machine learning within the Parkinson’s progression markers initiative: Review of the current state of affairs" @default.
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