Artificial intelligence methodologies and their application to diabetes

In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers?doctors and nurses?in this field

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

A Plasma glucose prediction tool based on dietary assessment: a case of type 2 diabetes patient

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore UniversityManagement and control of blood sugar using dietary intervention has for a long time been considered to be important. The caregivers have always advised diabetic patients to moderate the amount of carbohydrates intake. The approach here has always been reduction in the amount of carbohydrates, unfortunately this does not translate to the reduction on the blood sugar in some case. This is explained by the fact that what determines the sugar levels in the blood has to do more with the glycemic load of the carbohydrates consumed which is dependent on the glycemic index of the food item consumed. Though the amount of carbohydrates taken by the patient has a role to play, it is rather indirect. The study, sought to develop a tool for the computation of the glycemic load of the food item consumed by an individual by aggregating the various meals parameters. The tool has been developed by analyzing the dietary factors that affect the glycemic load and using these factors has the regressing variables. The algorithms used in the development of the dietary assessment tool have been used to map and mine the standard glycemic index of individual food item and to estimate individual patient meal item glycemic using regression analysis approach. Experimental data results indicate the tool can compute the glycemic load of the food item which is comparable to the standard glycemic load values and it also gives plasma glucose prediction trajectories which mirrors those obtained from existing clinical trial dataset

EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

A fusion-based machine learning approach for the prediction of the onset of diabetes

A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date

Improving nonlinear search with Self-Organizing Maps - Application to Magnetic Resonance Relaxometry

Quantification of myelin in vivo is crucial for the understanding of neurological diseases, like multiple sclerosis (MS). Multi-Component Driven Equilibrium Single Pulse Observation T1 and T2 (mcDESPOT) is a rapid and precise method for determination of the longitudinal and transverse relaxation times in a voxel wise fashion. Briefly, mcDESPOT couples sets of SPGR (spoiled gradient-recalled echo) and bSSFP (fully balance steady-state free precession) data acquired over a range of flip angles (α) with constant interpulse spacing (TR) to derive 6 parameters (free-water T1 and T2, myelin-associated water T1 and T2, relative myelin-associated water volume fraction, and the myelin-associated water proton residence time) based on water exchange models. However, this procedure is computationally expensive and extremely difficult due to the need to find the best fit to the 24 MRI signals volumes in a search of nonlinear 6 dimensional space of model parameters. In this context, the aim of this work is to improve mcDESPOT efficiency and accuracy using tissue information contained in the sets of signals (SPGR and bSSFP) acquired. The basic hypothesis is that similar acquired signals are referred to tissue portions with close features, which translate in similar parameters. This similarity could be used to drive the nonlinear mcDESPOT fitting, leading the optimization algorithm (that is based on a stochastic region contraction approach) to look for a solution (i.e. the 6 parameters vector) also in regions defined by previously computed solutions of others voxels with similar signals. For this reason, we clustered the sets of SPGR and bSSFP using the neural network called Self Organizing Map (SOM), which uses a competitive learning technique to train itself in an unsupervised manner. The similarity information obtained from the SOM was then used to accordingly suggest solutions to the optimization algorithm. A first validation phase with in silico data was performed to evaluate the performances of the SOM and of the modified method, SOM+mcDESPOT. The latter was further validated using real magnetic resonance images. The last step consisted of applying the SOM+mcDESPOT to a group of healthy subjects ( ) and a group of MS patients ( ) to look for differences in myelin-associated water fractions values between the two groups. The validation phases with in silico data verified the initial hypothesis: in more the 74% of the times, the correct solution of a certain voxel is in the space dictated by the cluster which that voxel is mapped to. Adding the information of similar solutions extracted from that cluster helps to improve the signals fitting and the accuracy in the determination of the 7 parameters. This result is still present even if the data are corrupted by a high level of noise (SNR=50). Using real images allowed to confirm the power of SOM+mcDESPOT underlined through the in silico data. The application of SOM+mcDESPOT to the controls and to the MS patients allowed firstly obtaining more feasible results than the traditional mcDESPOT. Moreover, a statistically significant difference of the myelin-associated water fraction values in the normal appearing white matter was found between the two groups: the MS patients, in fact, show lower fraction values compared to the normal subjects, indicating an abnormal presence of myelin in the normal appearing white matter of MS patients. In conclusion, we proposed the novel method SOM+mcDESPOT that is able to extract and exploit the information contained in the MRI signals to drive appropriately the optimization algorithm implemented in mcDESPOT. In so doing, the overall accuracy of the method in both the signals fitting and in the determination of the 7 parameters improves. Thus, the outstanding potentiality of SOM+mcDESPOT could assume a crucial role in improving the indirect quantification of myelin in both healthy subjects and patient

Analysis of closed - loop therapy in type ii diabetes.

La diabetes mellitus es una pandemia mundial, la cual ha incrementado su prevalencia en los últimos años. Esta enfermedad es principalmente caracterizada por el aumento de los niveles basales de glucosa en sangre, llamado hiperglicemia. De acuerdo a las causas, la diabetes mellitus puede ser clasificada en tres tipos: i) gestacional, relacionada con desbalances hormonales y metabólicos durante el ´ embarazo, ii) tipo 1, la cual es una enfermedad autoinmune caracterizada por la muerte progresiva de las celulas beta pancreaticas, las cuales se encargan de producir insulina, la hormona principal en el metabolismo de glucosa; y iii) tipo 2, la cual es una enfermedad metabolica caracterizada por el uso y producción disfuncional de la insulina. La diabetes mellitus tipo 2 es la mas recurrente, incluyendo mas del 95 % de casos clınicos. Algunos estudios recientes han mostrado que la implementación de la automatización en la dosificación de insulina para ´ pacientes con diabetes tipo 2 puede mejorar su tratamiento. Basándose en este ´ problema, el principal objetivo de esta tesis es desarrollar una metodología para analizar la viabilidad de tener una terapia en lazo cerrado para diabetes tipo 2. El estudio incluye la revisión de dos modelos matematicos del metabolismo de glucosa en sangre de gran utilidad para la síntesis de esquemas de control. Además, se presenta una metodología para personalizar este tipo de modelos basados en informació metabolica proveniente del monitoreo continuo de glucosa en pacientes con diabetes tipo 2. Después, se muestra el analisis de algunas caracterısticas de los modelos, su rol en la terapia de lazo cerrado y un caso de estudio usando un control convencional. ABSTRACT Diabetes mellitus is a worldwide pandemic, which prevalence has increased in the last years. This disease is mainly characterized by increased basal blood glucose levels, called hyperglycemia. According to the causes, diabetes mellitus can be classified in three types: i) gestational, related to hormonal and metabolic imbalances during pregnancy, ii) type 1 diabetes, which is an immune disease characterized by the progressive death of pancreatic βcells, that produce insulin, the principal hormone in glucose metabolism; and iii) type 2 diabetes, which is a metabolic disease characterized by dysfunction use and production of insulin. Type 2 diabetes mellitus is the most recurrent one, including more than 95 % of the clinical cases. Some recent studies have shown that the implementation of the automation of insulin dosage for type 2 diabetes patients can improve their treatment. Based on this problem, the main objective of this thesis is to develop a methodology to analyze the viability of having a closed-loop therapy for type 2 diabetes mellitus. The study includes the revision on two mathematical models of blood glucose metabolism useful to synthesize control schemes. Moreover, a methodology to personalize this kind of models based on metabolic data from continuous glucose monitoring of type 2 diabetic patients is presented. After that, the analysis of some characteristics of the models, their role in closed-loop therapy and a case of study using a conventional control scheme are presented

Chemical Bionics - a novel design approach using ion sensitive field effect transistors

In the late 1980s Carver Mead introduced Neuromorphic engineering in which various aspects of the neural systems of the body were modelled using VLSI1 circuits. As a result most bio-inspired systems to date concentrate on modelling the electrical behaviour of neural systems such as the eyes, ears and brain. The reality is however that biological systems rely on chemical as well as electrical principles in order to function. This thesis introduces chemical bionics in which the chemically-dependent physiology of specific cells in the body is implemented for the development of novel bio-inspired therapeutic devices. The glucose dependent pancreatic beta cell is shown to be one such cell, that is designed and fabricated to form the first silicon metabolic cell. By replicating the bursting behaviour of biological beta cells, which respond to changes in blood glucose, a bio-inspired prosthetic for glucose homeostasis of Type I diabetes is demonstrated. To compliment this, research to further develop the Ion Sensitive Field Effect Transistor (ISFET) on unmodified CMOS is also presented for use as a monolithic sensor for chemical bionic systems. Problems arising by using the native passivation of CMOS as a sensing surface are described and methods of compensation are presented. A model for the operation of the device in weak inversion is also proposed for exploitation of its physical primitives to make novel monolithic solutions. Functional implementations in various technologies is also detailed to allow future implementations chemical bionic circuits. Finally the ISFET integrate and fire neuron, which is the first of its kind, is presented to be used as a chemical based building block for many existing neuromorphic circuits. As an example of this a chemical imager is described for spatio-temporal monitoring of chemical species and an acid base discriminator for monitoring changes in concentration around a fixed threshold is also proposed