Deep digital phenotyping in type 1 diabetes: The reinvention of epidemiological research

Research output: Contribution to journalShort surveypeer-review

1 Citation (Scopus)

Abstract

Digital technology offers new opportunities to better characterize, the reality of daily life of people with type 1 diabetes. The “digitosome”, the digital data generated online by individuals during their lifetime, is an unprecedented source of information to be analyzed using artificial intelligence methods (digital phenotyping), to be combined with other clinical and omics data (deep phenotyping). Thus, this concept of deep digital phenotyping will make it possible to obtain more holistic characterizations of people with diabetes and then to identify, in large cohorts, their digital twin and offer them precision health programs, personalized diabetes and therapeutic management. These concepts, as well as the vigilance required to guard against possible drifts related to the misuse of data and to artificial intelligence are discussed in this article based on current research developments.

Original languageEnglish
Pages (from-to)375-379
Number of pages5
JournalMedecine des Maladies Metaboliques
Volume15
Issue number4
DOIs
Publication statusPublished - Jun 2021

Keywords

  • Artificial intelligence
  • Cohort
  • Data
  • Digital twin
  • Epidemiology

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