Data Mining Practical Machine Learning Tools and Techniques

This paper presents a comprehensive survey about the fundamental components of the artificial pancreas (AP) system including insulin administration and delivery, glucose measurement (GM), and control strategies/algorithms used for type 1 diabetes mellitus (T1DM) treatment and control. Our main focus is on the T1DM that emerges due to pancreas’s failure to produce sufficient insulin due to the loss of beta cells (β-cells). We discuss various insulin administration and delivery methods including physiological methods, open-loop, and closed-loop schemes. Furthermore, we report several factors such as hyperglycemia, hypoglycemia, and many other physical factors that need to be considered while infusing insulin in human body via AP systems. We discuss three prominent control algorithms including proportional-integral- derivative (PID), fuzzy logic, and model predictive, which have been clinically evaluated and have all shown promising results. In addition, linear and non-linear insulin infusion control schemes have been formally discussed. To the best of our knowledge, this is the first work which systematically covers recent developments in the AP components with a solid foundation for future studies in the T1DM field.

https://www.mdpi.com/2571-5577/3/3/31/pdf

Artificial Pancreas Control Strategies Used for Type 1 Diabetes Control and Treatment: A Comprehensive Analysis

Glucose forecasting using genetic programming and latent glucose variability features

Blood glucose management is a difficult task that people with diabetes usually have to perform by themselves. An accurate and timely prediction is vital in order to take decisions and recommend corrective actions to the patient when the future blood glucose value lies outside of a target range. It is crucial to predict events like hyperglycemia and hypoglycemia that may cause health damages in the short term and potential permanent damages in the long term. The aim of this paper is to describe our research on predicting blood glucose values using a two phase Takagi-Sugeno-Kang Fuzzy Rule Based System. The first phase is a learning process where membership functions and rules are optimised by a genetic algorithm. In the second phase of tuning, we used a genetic algorithm to perform the selection and optimisation of the rules. To train our model we used two different scenarios, What-if and Agnostic; in both of them the inputs are values measured by a continuous monitoring glucose system as well as previous carbohydrate intake and insulin injections. In the What-if scenario, assumed future values of meals and insulin injections are permitted. On the other hand, in the Agnostic scenario, only information of the past and present events are available for the prediction. We trained and tested our system with real data collected from 10 different diabetic patients, producing 30, 60, 90 and 120 minutes predictions with encouraging accuracy results.

Esther Maqueda Villaizán

Papers

Identificación de patrones de glucemia en pacientes con diabetes tipo1 mediante monitorización continua de glucosa y técnicas de clusterización

Identification of blood glucose patterns in patients with type 1 diabetes using continuous glucose monitoring and clustering technique