Virtual Cohorts and Synthetic Data in Dementia: An Illustration of Their Potential to Advance Research.
| Autor: | Muniz-Terrera G; Edinburgh Dementia Prevention Group, University of Edinburgh, Edinburgh, United Kingdom., Mendelevitch O; Syntegra, San Carlos, CA, United States., Barnes R; Aridhia Informatics, Glasgow, United Kingdom., Lesh MD; Syntegra, San Carlos, CA, United States.; University of California San Francisco, Mill Valley, CA, United States. |
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| Jazyk: | angličtina |
| Zdroj: | Frontiers in artificial intelligence [Front Artif Intell] 2021 May 17; Vol. 4, pp. 613956. Date of Electronic Publication: 2021 May 17 (Print Publication: 2021). |
| DOI: | 10.3389/frai.2021.613956 |
| Abstrakt: | When attempting to answer questions of interest, scientists often encounter hurdles that may stem from limited access to existing adequate datasets as a consequence of poor data sharing practices, constraining administrative practices. Further, when attempting to integrate data, differences in existing datasets also impose challenges that limit opportunities for data integration. As a result, the pace of scientific advancements is suboptimal. Synthetic data and virtual cohorts generated using innovative computational techniques represent an opportunity to overcome some of these limitations and consequently, to advance scientific developments. In this paper, we demonstrate the use of virtual cohorts techniques to generate a synthetic dataset that mirrors a deeply phenotyped sample of preclinical dementia research participants. Competing Interests: OM and ML are employed by the company Syntegra. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2021 Muniz-Terrera, Mendelevitch, Barnes and Lesh.) |
| Databáze: | MEDLINE |
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