PhD thesis defense to be held on February 14, 2023, at 16:30 (Multimedia/Tele-teaching room, NTUA Central Library Building)

Picture Credit: Maria Athanasiou

Thesis title: Development of Interpretable Machine Learning Models to Support Diabetes Management

Abstract: The present thesis aims at the design, development, and evaluation of interpretable machine learning models to support decision making in Health. The proposed methods leverage heterogeneous data and address issues such as the unbalanced nature of the available data and the need to produce interpretable decisions to enable the adoption of the models in health decision making. Considering the epidemiological model of Diabetes Mellitus (DM) and the range of clinical use cases it entails, the metabolic disorder of DM is selected for model development and evaluation. More specifically, data from Electronic Health Records (EHR), laboratory measurements, and glucose-insulin records are utilized towards the development of interpretable models able to assess the health status of people with DM as well as of computational systems supporting people with DM in achieving optimal glycemic control. The first research direction focuses on the development of interpretable risk prediction models for (i) the incidence of Cardiovascular Disease (CVD) in patients with Type 2 Diabetes Mellitus (T2DM) and (ii) the assessment of the risk of hospitalization and re-hospitalization due to Diabetic Ketoacidosis (DKA) or Hyperglycemia with Ketosis (HK) in patients with Type 1 Diabetes Mellitus (T1DM). In the second direction, personalized systems are proposed for automated meal detection and the estimation of prandial insulin boluses in individuals with T1DM.

Supervisor: Professor Konstantina Nikita

PhD Student: Maria Athanasiou