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Identifying the necessary capacities for the adaptation of a diabetes phenotyping algorithm in countries of differing economic development status.

Angela Mary Jackson-MorrisRita SembajweFeisul Idzwan MustaphaArunah ChandranSimon Pierre NiyonsengaCrispin GishomaElizabeth OnyangoZachariah MuriukiKavita DharamrajNathan EllermeierRachel A NugentRasa Kazlauskaite
Published in: Global health action (2023)
Malaysia was found to be most ready to apply the phenotyping algorithm. A fundamental impediment in the other settings was the absence of several core diabetes data variables. Additionally, if countries digitise diabetes data collection and centralise diabetes data hosting, this will simplify dataset preparation for algorithm application. These issues reflect common LMIC health systems' weaknesses in relation to diabetes care, and specifically highlight the importance of investment in improving diabetes data, which can guide population-tailored prevention and management approaches.
Keyphrases
  • type diabetes
  • cardiovascular disease
  • glycemic control
  • electronic health record
  • machine learning
  • big data
  • deep learning
  • high throughput
  • adipose tissue
  • neural network
  • high resolution
  • life cycle