Personalized data-driven prediction algorithms for glycemic trend : doctoral thesis - Žulj, Sara (S. Žulj ; Faculty of electrical engineering and computing, 2022)
An important aspect of diabetes management is controlling the glucose level. A step towards achieving the control is monitoring and taking actions (e.g., administering insulin) to avoid dangerous levels. Predicting future values may allow the user to be alerted when an action is needed. This thesis investigates an interpretable case-based reasoning (CBR) model for predicting future glucose levels. The focus of this work lies in exploring the different hyperparameters of CBR model. The work also ev104 - 117aluates the effect of meal intake as an input variable in a data-driven way. This is in contrast to previously published research which use dedicated physiological models to include the meal. The effect of the dataset size on the prediction error is modelled. Models are often trained on very small datasets and reported error often does not reflect the large variance caused by the dataset size. Further, the personalization of the CBR model by clustering the patient data prior to building the CBR model’s case base is studied. The clustering is done using clinical, behavioral, anthropometric and demographic data. Finally, a new two-stage method for maintaining the case base of the CBR regression problem is proposed. The contributions are: • A case-based reasoning model for prediction of glucose levels, • A personalization of the model through a clustering strategy, • A method for updating and maintaining the case base.