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

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

Intelligent decision support systems for optimised diabetes

Computers now pervade the field of medicine extensively; one recent innovation is the development of intelligent decision support systems for inexperienced or non-specialist pbysicians, or in some cases for use by patients. In this thesis a critical review of computer systems in medicine, with special reference to decision support systems, is followed by a detailed description of the development and evaluation of two new, interacting, intelligent decision support systems in the domain of diabetes. Since the discovery of insulin in 1922, insulin replacement therapy for the treatment of diabetes mellitus bas evolved into a complex process; there are many different formulations of insulin and much more information about the factors which affect patient management (e.g. diet, exercise and progression of complications) are recognised. Physicians have to decide on the most appropriate anti-diabetic therapy to prescribe to their patients. Insulin-treated patients also have to monitor their blood glucose and decide how much insulin to inject and when to inject it. In order to help patients determine the most appropriate dose of insulin to take, a simple-to-use, hand-held decision support system has been developed. Algorithms for insulin adjustment have been elicited and combined with general rules of therapy to offer advice for every dose. The utility of the system has been evaluated by clinical trials and simulation studies. In order to aid physician management, a clinic-based decision support system has also been developed. The system provides wide-ranging advice on all aspects of diabetes care and advises an appropriate therapy regimen according to individual patient circumstances. Decisions advised by the pbysician-related system have been evaluated by a panel of expert physicians and the system has undergone informal primary evaluation within the clinic setting. An interesting aspect of both systems is their ability to provide advice even in cases where information is lacking or uncertain

Deep learning methods for improving diabetes management tools

Diabetes is a chronic disease that is characterised by a lack of regulation of blood glucose concentration in the body, and thus elevated blood glucose levels. Consequently, affected individuals can experience extreme variations in their blood glucose levels with exogenous insulin treatment. This has associated debilitating short-term and long-term complications that affect quality of life and can result in death in the worst instance. The development of technologies such as glucose meters and, more recently, continuous glucose monitors have offered the opportunity to develop systems towards improving clinical outcomes for individuals with diabetes through better glucose control. Data-driven methods can enable the development of the next generation of diabetes management tools focused on i) informativeness ii) safety and iii) easing the burden of management. This thesis aims to propose deep learning methods for improving the functionality of the variety of diabetes technology tools available for self-management. In the pursuit of the aforementioned goals, a number of deep learning methods are developed and geared towards improving the functionality of the existing diabetes technology tools, generally classified as i) self-monitoring of blood glucose ii) decision support systems and iii) artificial pancreas. These frameworks are primarily based on the prediction of glucose concentration levels. The first deep learning framework we propose is geared towards improving the artificial pancreas and decision support systems that rely on continuous glucose monitors. We first propose a convolutional recurrent neural network (CRNN) in order to forecast the glucose concentration levels over both short-term and long-term horizons. The predictive accuracy of this model outperforms those of traditional data-driven approaches. The feasibility of this proposed approach for ambulatory use is then demonstrated with the implementation of a decision support system on a smartphone application. We further extend CRNNs to the multitask setting to explore the effectiveness of leveraging population data for developing personalised models with limited individual data. We show that this enables earlier deployment of applications without significantly compromising performance and safety. The next challenge focuses on easing the burden of management by proposing a deep learning framework for automatic meal detection and estimation. The deep learning framework presented employs multitask learning and quantile regression to safely detect and estimate the size of unannounced meals with high precision. We also demonstrate that this facilitates automated insulin delivery for the artificial pancreas system, improving glycaemic control without significantly increasing the risk or incidence of hypoglycaemia. Finally, the focus shifts to improving self-monitoring of blood glucose (SMBG) with glucose meters. We propose an uncertainty-aware deep learning model based on a joint Gaussian Process and deep learning framework to provide end users with more dynamic and continuous information similar to continuous glucose sensors. Consequently, we show significant improvement in hyperglycaemia detection compared to the standard SMBG. We hope that through these methods, we can achieve a more equitable improvement in usability and clinical outcomes for individuals with diabetes.Open Acces

Charakteristika vertikálně uspořádaných uhlíkových nanotrubic jako platforem pro aplikace v oblasti biosenzorů

Univerzita Karlova v Praze, Farmaceutická fakulta v Hradci Králové Katedra Analytické chemie Kandidát: Tereza Štipková Školitel: doc. PharmDr. Lucie Nováková, Ph.D. Školitel specialista: Assist. Prof. Gulnara Safina, Ph.D. Název diplomové práce: Charakteristika vertikálně uspořádaných uhlíkových nanotrubic jako platforem pro aplikace v oblasti biosenzorů Monitorování glukózy v krvi je nezbytnou součástí moderní terapie diabetu. V této práci byly použity mnohostěnné vertikálně uspořádané uhlíkové nanotrubice (CNT) pro konstrukci nového biosenzoru třetí generace. Hlavním zaměřením práce bylo zjištění možnosti uplatnění senzoru pro monitorování glukosy. Amperometrický biosenzor na bázi CNT byl vyvinut imobilizací celobiosadehydrogenasy z houby Corynascus thermophilus na povrch CNT pomocí fyzikální adsorpce. Vysoce hydrofobní povrch nanotrubic byl modifikován 0,0001% (w/v) polyvinylalkoholem (PVA) za účelem umožnění adsorpce enzymu. Výsledky měření pomocí rentgenové fotoelektronové spektroskopie potvrdily úspěšnou imobilizaci enzymu na povrch CNT modifikován PVA. Topografie povrchu a elementární složení nanomateriálu byly zkoumány rastrovacím elektronovým mikroskopem a metodou energiově disperzní spektroskopie. Optimální podmínky pro elektrochemické měření v průtokové cele s nástřiky glukosy zahrnovaly...Charles University in Prague, Faculty of Pharmacy in Hradec Králové Department of Analytical Chemistry Candidate: Tereza Štipková Supervisor: Assoc. Prof. PharmDr. Lucie Nováková, Ph.D. Supervisor specialist: Assist. Prof. Gulnara Safina, Ph.D. Title of thesis: Working performance of vertically aligned carbon nanotubes as platforms for future biosensing applications Blood glucose monitoring is a crucial part of a modern diabetes therapy. In this work, the multi-walled vertically aligned on-chip grown carbon nanotubes (CNT) were applied in construction of a novel third-generation biosensor. The main focus of this work was to investigate the possibility of future application of the sensor in glucose monitoring. The CNT- based amperometric biosensor was developed by immobilising cellobiose dehydrogenase from Corynascus thermophilus onto the CNT surface by physical adsorption. The highly hydrophobic surface of the nanotubes was functionalized with 0.0001% (w/v) polyvinyl alcohol (PVA) in order to enable the enzyme adsorption. X-ray photoelectron spectroscopy measurements confirmed the successful enzyme immobilization onto PVA modified CNT surface. The surface topography and elemental composition of the nanomaterial were examined by scanning electron microscopy and energy-dispersive X-ray spectroscopy...Department of Analytical ChemistryKatedra analytické chemieFarmaceutická fakulta v Hradci KrálovéFaculty of Pharmacy in Hradec Králov

Framework para el desarrollo y entrenamiento de sistemas de indeferencia difusa siguiendo métodos de desarrollo dirigido por modelos

224 p.Este trabajo de tesis doctoral presenta un modelo independiente de la computación de un Diagnóstico Diferencial (DD), así como un modelo independiente de la plataforma de un Sistema de Inferencia Difusa. Se han utilizado los Métodos de Desarrollo Dirigido por Modelos (MDDM) en la concepción de los modelos, los cuales, además de facilitar la definición de los modelos, ofrecen herramientas para la realización de transformaciones entre ellos. Así, en el presente trabajo también se exponen las transformaciones entre los modelos de DD y SID y las transformaciones para la generación automática de SID expresados en lenguajes concretos a partir de los modelos de SID independientes de la plataforma. Los SID dependientes de la plataforma pueden ser incluidos en el formalismo de representación de Guías Clínicas Informatizadas (GCI) Aide. Así mismo, en la tesis también se incluye una descripción de las herramientas que facilitan la definición de modelos de DD y SID, así como la generación automática de SID en lenguajes concretos utilizables en distintos motores de razonamiento. Es de reseñar la adición de un módulo de aprendizaje automático mediante un Algoritmo Genético que permite adaptar algunas características de los modelos de SID a los datos reales de entrenamiento. Las herramientas y modelos se han validado en dos ámbitos. Por un lado, se han utilizado en el cribado neonatal, una prueba diagnóstica dirigida a la identificación presintomática de enfermedades graves con el fin de tratarlas precozmente y así prevenir y minimizar minusvalías neurológicas, orgánicas y psíquicas. Por otro lado, se han utilizado en el diagnóstico de la hiperamonemia, una Enfermedad Rara que se debe tratar de forma urgente para evitar graves secuelas neurológicas e incluso la muerte. En ambos casos, los SID creados se han integrado en unas GCI para ser evaluados

Framework para el desarrollo y entrenamiento de sistemas de indeferencia difusa siguiendo métodos de desarrollo dirigido por modelos

224 p.Este trabajo de tesis doctoral presenta un modelo independiente de la computación de un Diagnóstico Diferencial (DD), así como un modelo independiente de la plataforma de un Sistema de Inferencia Difusa. Se han utilizado los Métodos de Desarrollo Dirigido por Modelos (MDDM) en la concepción de los modelos, los cuales, además de facilitar la definición de los modelos, ofrecen herramientas para la realización de transformaciones entre ellos. Así, en el presente trabajo también se exponen las transformaciones entre los modelos de DD y SID y las transformaciones para la generación automática de SID expresados en lenguajes concretos a partir de los modelos de SID independientes de la plataforma. Los SID dependientes de la plataforma pueden ser incluidos en el formalismo de representación de Guías Clínicas Informatizadas (GCI) Aide. Así mismo, en la tesis también se incluye una descripción de las herramientas que facilitan la definición de modelos de DD y SID, así como la generación automática de SID en lenguajes concretos utilizables en distintos motores de razonamiento. Es de reseñar la adición de un módulo de aprendizaje automático mediante un Algoritmo Genético que permite adaptar algunas características de los modelos de SID a los datos reales de entrenamiento. Las herramientas y modelos se han validado en dos ámbitos. Por un lado, se han utilizado en el cribado neonatal, una prueba diagnóstica dirigida a la identificación presintomática de enfermedades graves con el fin de tratarlas precozmente y así prevenir y minimizar minusvalías neurológicas, orgánicas y psíquicas. Por otro lado, se han utilizado en el diagnóstico de la hiperamonemia, una Enfermedad Rara que se debe tratar de forma urgente para evitar graves secuelas neurológicas e incluso la muerte. En ambos casos, los SID creados se han integrado en unas GCI para ser evaluados

Learning Outcomes of Classroom Research

Personal pronouns are a linguistic device that is used to engage students at various educational levels. Personal pronouns are multifunctional, and their functions range from inclusion to exclusion, and include establishing of rapport with students. In this chapter, we compare the use of personal pronouns at university and secondary school levels. Our previous study (Yeo & Ting, 2014) showed the frequent use of you in lecture introductions (2,170 instances in the 37,373-word corpus) to acknowledge the presence of students. The arts lecturers were more inclusive than the science lecturers, reflected in the less frequent use of exclusive-we and we for one, as well as the frequent use of you-generalised. We have also compiled and analysed a 43,511-word corpus from 15 English lessons in three Malaysian secondary schools. This corpus yielded 2,019 instances of personal pronoun use. The results showed that you was the most frequently used personal pronoun, followed by we and I. You-audience was used more than you-generalised, and the main function was to give instructions to students. The teachers appeared to be more directive than the lecturers in the previous study, who sometimes used the inclusive-we for you and I and we for I to lessen the social distance with students, indicating that the discourse functions of personal pronouns vary with the educational context. The findings suggest that educators can be alerted to the versatility of personal pronouns, for example, for engaging students in the lesson and for asserting authority in the subject matter. Keywords: student engagement; personal pronouns; lecture; classroom; teache