Multi-Input Multi-Output Dynamic Modelling of Type 2 Diabetes Progression

Davide Simeone, Marta Lenatti, Constantino Lagoa, Karim Keshavjee, Aziz Guergachi, Fabrizio Dabbene, Alessia Paglialonga

Research output: Contribution to journalArticlepeer-review

Abstract

Type 2 Diabetes Mellitus (T2D) is a chronic health condition that affects millions of people globally. Early identification of risk can support preventive intervention and therefore slow down disease progression. Risk characterization is also necessary to monitor the mechanisms behind the pathology through the analysis of the interrelationships between the predictors and their time course. In this work, a multi-input multi-output Gaussian Process model is proposed to describe the evolution of different biomarkers in patients who will/will not develop T2D considering the interdependencies between outputs. The preliminary results obtained suggest that the trends in biomarkers captured by the model are coherent with the literature and with real-world data, demonstrating the value of multi-input multi-output approaches. In future developments, the proposed method could be applied to assess how the biomarkers evolve and interact with each other in groups of patients having in common one or more risk factors.

Original languageEnglish (US)
Pages (from-to)228-232
Number of pages5
JournalStudies in Health Technology and Informatics
Volume309
DOIs
StatePublished - Oct 20 2023

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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