Causality analysis in type 1 diabetes mellitus with application to blood glucose level prediction. (February 2023)

• Record Type: Journal Article
• Authors: Nemat, Hoda; Khadem, Heydar; Elliott, Jackie; Benaissa, Mohammed
• Is Part Of: Computers in biology and medicine. Volume 153(2023)
• Publication Date: 2023-02
• Subjects: Blood glucose level -- Causality analysis -- Deep learning -- Diabetes mellitus -- Time series forecasting; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106535
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Effective control of blood glucose level (BGL) is the key factor in the management of type 1 diabetes mellitus (T1D). BGL prediction is an important tool to help maximise the time BGL is in the target range and thus minimise both acute and chronic diabetes-related complications. To predict future BGL, histories of variables known to affect BGL, such as carbohydrate intake, injected bolus insulin, and physical activity, are utilised. Due to these identified cause and effect relationships, T1D management can be examined via the causality context. In this respect, this work initially investigates these relations and quantifies the causality strengths of each variable with BGL using the convergent cross mapping method (CCM). Then, considering the extended CCM, the causality strengths of each variable for different lags are quantified. After that, the optimal time lag for each variable is determined according to the quantified causality effects. Subsequently, the feasibility of leveraging causality information as prior knowledge for BGL prediction is investigated by proposing two approaches. In the first approach, causality strengths are used as weights for relevant affecting variables. In the second approach, the optimal causal lags and the corresponding causality strengths are considered the shifts and weights for the variables, respectively. Overall, the evaluation criteria and statistical analysis used for comparing results show the effectiveness of using causality analysis in T1D management. Highlights: The relations in T1D management are investigated within a causality framework. Causal relations of affecting variables on BGL are quantified using convergent cross mapping. Effective causal delays are determined using extended convergent cross mapping. The quantified causal information is deployed to improve the performance of BGL prediction.