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Machine Learning prediction of diabetes comorbidities in a large Italian cohort
Diabetes mellitus (DM) and its complications are one of the leading causes of death worldwide. Early detection is crucial to prevent or mitigate its prognosis. However, DM is often diagnosed late and after decades of silent deterioration of the body. Here we use AI to predict five selected diabetes comorbidities on 17,000 routine blood tests with 23 early-accessible variables, none of which is being currently used as a diagnostic criterion for the complications. The promising results support a potential use to empower diagnostic tools and facilitate the early detection of DM complications.
Giacomo Bornino, Marco Chierici, Venet Osmani, Antonio Colangelo, Giuseppe Jurman
Marco Chierici (Ph.D. Bioengineering) is a senior data scientist at the Data Science for Health (DSH) research unit of FBK. His main research interests include the integration of AI in bioinformatics & computational biology frameworks. Marco co-authored >40 papers on peer-reviewed journals. The presented project is a joint work with Giacomo Bornino (postgraduate student UniTN), Venet Osmani (senior researcher FBK), Giuseppe Jurman (senior researcher FBK, head of DSH unit), and Antonio Colangelo (Scientific Director, Research Center GPI).
