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.
Keyphrases
- type diabetes
- risk factors
- healthcare
- randomized controlled trial
- systematic review
- public health
- ejection fraction
- insulin resistance
- adipose tissue
- mental health
- glycemic control
- end stage renal disease
- prognostic factors
- metabolic syndrome
- machine learning
- deep learning
- artificial intelligence
- cardiovascular risk factors