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https://hdl.handle.net/10316/103994| Title: | Big data and machine learning to tackle diabetes management | Authors: | Pina, Ana F Meneses, Maria João Sousa-Lima, Inês Henriques, Roberto Raposo, João F Macedo, Maria Paula |
Keywords: | big data; cluster analysis; diabetes; machine learning | Issue Date: | 17-Oct-2022 | Project: | PD/BD/136887/2018 PTDC/MEC-MET/29314/2017 PTDC/BIM-MET/2115/2014 UIDB/Multi/04462/2020 info:eu-repo/grantAgreement/EC/H2020/722619 |
metadata.degois.publication.volume: | 53 | metadata.degois.publication.issue: | 1 | Abstract: | Type 2 Diabetes (T2D) diagnosis is based solely on glycaemia, even though it is an endpoint of numerous dysmetabolic pathways. Type 2 Diabetes complexity is challenging in a real-world scenario; thus, dissecting T2D heterogeneity is a priority. Cluster analysis, which identifies natural clusters within multidimensional data based on similarity measures, poses a promising tool to unravel Diabetes complexity. | URI: | https://hdl.handle.net/10316/103994 | ISSN: | 0014-2972 1365-2362 |
DOI: | 10.1111/eci.13890 | Rights: | embargoedAccess |
| Appears in Collections: | I&D CNC - Artigos em Revistas Internacionais |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| BigDataandMachineLearning.pdf | 1.24 MB | Adobe PDF | View/Open |
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