Neural multi-class classification approach to blood glucose level forecasting with prediction uncertainty visualisation

A machine learning-based method for blood glucose level prediction thirty and sixty minutes in advance based on highly multiclass classification (as opposed to the more traditional regression approach) is proposed. An advantage of this approach is the possibility of modelling and visualising the uncertainty of a prediction across the entire range of blood glucose levels without parametric assumptions such as normality. To demonstrate the approach, a long-short term memory-based neural network classifier is used in conjunction with a blood glucose-specific data preprocessing technique (risk domain transform) to train a set of models and generate predictions for the 2018 and 2020 Blood Glucose Level Prediction Competition datasets. Numeric accuracy results are reported along with examples of the uncertainty visualisation possible using this technique

Predicting glucose level with an adapted branch predictor

Background and objective Diabetes mellitus manifests as prolonged elevated blood glucose levels resulting from impaired insulin production. Such high glucose levels over a long period of time damage multiple internal organs. To mitigate this condition, researchers and engineers have developed the closed loop artificial pancreas consisting of a continuous glucose monitor and an insulin pump connected via a microcontroller or smartphone. A problem, however, is how to accurately predict short term future glucose levels in order to exert efficient glucose-level control. Much work in the literature focuses on least prediction error as a key metric and therefore pursues complex prediction methods such a deep learning. Such an approach neglects other important and significant design issues such as method complexity (impacting interpretability and safety), hardware requirements for low-power devices such as the insulin pump, the required amount of input data for training (potentially rendering the method infeasible for new patients), and the fact that very small improvements in accuracy may not have significant clinical benefit. Methods We propose a novel low-complexity, explainable blood glucose prediction method derived from the Intel P6 branch predictor algorithm. We use Meta-Differential Evolution to determine predictor parameters on training data splits of the benchmark datasets we use. A comparison is made between our new algorithm and a state-of-the-art deep-learning method for blood glucose level prediction. Results To evaluate the new method, the Blood Glucose Level Prediction Challenge benchmark dataset is utilised. On the official test data split after training, the state-of-the-art deep learning method predicted glucose levels 30 min ahead of current time with 96.3% of predicted glucose levels having relative error less than 30% (which is equivalent to the safe zone of the Surveillance Error Grid). Our simpler, interpretable approach prolonged the prediction horizon by another 5 min with 95.8% of predicted glucose levels of all patients having relative error less than 30%. Conclusions When considering predictive performance as assessed using the Blood Glucose Level Prediction Challenge benchmark dataset and Surveillance Error Grid metrics, we found that the new algorithm delivered comparable predictive accuracy performance, while operating only on the glucose-level signal with considerably less computational complexity

Genetic Programming-based induction of a glucose-dynamics model for telemedicine

This paper describes our preliminary steps towards the deployment of a brand-new original feature for a telemedicine portal aimed at helping people suffering from diabetes. In fact, people with diabetes necessitate careful handling of their disease to stay healthy. As such a disease is correlated to a malfunction of the pancreas that produces very little or no insulin, a way to enhance the quality of life of these subjects is to implement an artificial pancreas able to inject an insulin bolus when needed. The goal of this paper is to extrapolate a regression model, capable of estimating the blood glucose (BG) through interstitial glucose (IG) measurements, that represents a possible revolutionizing step in constructing the fundamental element of such an artificial pancreas. In particular, a new evolutionary approach is illustrated to stem a mathematical relationship between BG and IG. To accomplish the task, an automatic evolutionary procedure is also devised to estimate the missing BG values within the investigated real-world database made up of both BG and IG measurements of people suffering from Type 1 diabetes. The discovered model is validated through a comparison with other models during the experimental phase on global and personalized data treatment. Moreover, investigation is performed about the accuracy of one single global relationship model for all the subjects involved in the study, as opposed to that obtained through a personalized model found for each of them. Once this research is clinically validated, the important feature of estimating BG will be added to a web portal for diabetic subjects for telemedicine purposes

Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment

Diabetes is a heterogeneous group of diseases that share a common trait of elevated blood glucose levels. Insulin lowers this level by promoting glucose utilization, thus avoiding short- and long-term organ damage due to the elevated blood glucose level. A patient with diabetes uses an insulin pump to dose insulin. The pump uses a controller to compute and dose the correct amount of insulin to keep blood glucose levels in a safe range. Insulin-pump controller development is an ongoing process aiming at fully closed-loop control. Controllers entering the market must be evaluated for safety. We propose an evaluation method that exploits an FDA-approved diabetic patient simulator. The method evaluates a Cartesian product of individual insulin-pump parameters with a fine degree of granularity. As this is a computationally intensive task, the simulator executes on a distributed cluster. We identify safe and risky combinations of insulin-pump parameter settings by applying the binomial model and decision tree to this product. As a result, we obtain a tool for insulin-pump settings and controller safety assessment. In this paper, we demonstrate the tool with the Low-Glucose Suspend and OpenAPS controllers. For average ± standard deviation, LGS and OpenAPS exhibited 1.7 ± 0.6% and 3.2 ± 1.8% of local extrema (i.e., good insulin-pump settings) out of all the entire Cartesian products, respectively. A continuous region around the best-discovered settings (i.e., the global extremum) of the insulin-pump settings spread across 4.0 ± 1.1% and 4.1 ± 1.3% of the Cartesian products, respectively

A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction

V tomto článku navrhujeme inovativní evoluční rámec inspirovaný federativním učením. práci. Jeho hlavní novinkou je to, že je to poprvé, kdy je evoluční algoritmus použit na sám o sobě k přímému provádění činnosti federativního učení. Další novinka spočívá v tom, že, na rozdíl od ostatních rámců federativního učení v literatuře se ten náš dokáže efektivně vypořádat současně se dvěma relevantními problémy v oblasti strojového učení, tj. soukromím dat a interpretovatelností řešení. Náš rámec se skládá z přístupu master/slave, v němž každý slave obsahuje lokální data, chránící rozumná soukromá data, a využívá evoluční algoritmus k vytváření predikčních modelů. Master sdílí prostřednictvím slave lokálně naučené modely, které vznikají na každém slave. Výsledkem sdílení těchto lokálních modelů jsou globální modely. Vzhledem k tomu, že soukromí a interpretovatelnost dat jsou v lékařské oblasti velmi významné, je algoritmus testován k předpovídání budoucích hodnot glukózy u diabetických pacientů s využitím algoritmu gramatické evoluce. Účinnost tohoto procesu sdílení znalostí je hodnocena experimentálně porovnáním navrhovaného rámce s jiným, kde k výměně lokálních modelů nedochází. Výsledky ukazují, že výkonnost navrhovaného přístupu je lepší, a prokazují oprávněnost jeho procesu sdílení pro vznik lokálních modelů pro osobní léčbu diabetu, využitelných jako účinné globální modely. Vezmeme-li v úvahu další subjekty nezapojené do procesu učení, vykazují modely objevené naším rámcem vyšší schopnost generalizace než modely dosažené bez sdílení znalostí: zlepšení, které poskytuje sdílení znalostí, se rovná přibližně 3,03 % pro přesnost, 1,56 % pro odvolání, 3,17 % pro F1 a 1,56 % pro přesnost. Statistická analýza navíc odhaluje statistickou převahu výměny modelů nad případem, kdy k výměně nedochází.In this paper, we propose an innovative Federated Learning-inspired evolutionary frame- work. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the literature, ours can efficiently deal at the same time with two relevant issues in Machine Learning, i.e., data privacy and interpretability of the solutions. Our framework consists of a master/slave approach in which each slave contains local data, protecting sensible private data, and exploits an evolutionary algorithm to generate prediction models. The master shares through the slaves the locally learned models that emerge on each slave. Sharing these local models results in global models. Being that data privacy and interpretability are very significant in the medical domain, the algorithm is tested to forecast future glucose values for diabetic patients by exploiting a Grammatical Evolution algorithm. The effectiveness of this knowledge-sharing process is assessed experimentally by comparing the proposed framework with another where no exchange of local models occurs. The results show that the performance of the proposed approach is better and demonstrate the validity of its sharing process for the emergence of local models for personal diabetes management, usable as efficient global models. When further subjects not involved in the learning process are considered, the models discovered by our frame-work show higher generalization capability than those achieved without knowledge sharing: the improvement provided by knowledge sharing is equal to about 3.03% for precision, 1.56% for recall, 3.17% for F1, and 1.56% for accuracy. Moreover, statistical analysis reveals the statistical superiority of model exchange with respect to the case of no exchange taking place