An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

Managing Challenges of Non Communicable Diseases during Pregnancy: An Innovative Approach

Le sfide lanciate dalle malattie non trasmissibili sono accolte da tecnologie sempre più all'avanguardia. Nonostante questo, ancora oggi gestire e monitorare gravidanze a rischio rimane un problema. La simulazione di condizioni come quella data dal diabete gestazionale, può aiutare a capire quali sono i principali fattori che influenzano l'andamento della malattia in modo da poterne evitare l'insorgenza e, in questo modo, migliorare la salute di madri e generazioni future. Questa tesi ha come obietto lo studio e il miglioramento di un sistema Agent-Based pensato per il trattamento del diabete di tipo 1 e la modellazione di una sua estensione per il diabete gestazionale. Al termine della tesi è stato migliorato il sistema rendendolo più fedele ai cambiamenti fisiologici che avvengono durante il metabolismo del glucosio e la modellazione della placenta e relativamente delle modifiche che apporta all'intero sistema getta le basi per nuovi sviluppi legati al trattamento di malattie durante il periodo di gestazione

Diabetes Management System for a New Type 2 Diabetes Geriatric Cohort: Improve the Interaction of Self-management

abstract: According to the ADA (American Diabetes Association), diabetes mellitus is one of the chronic diseases with the highest mortality rate. In the US, 25 million are known diabetics, which may double in the next decade, and another seven million are undiagnosed. Among these patients, older adults are a very special group with varying physical capabilities, cognitive functions and life expectancies. Because they run an increased risk for geriatric conditions, Type 2 diabetes treatments for them must be both realistic and systematic. In fact, some researchers have explored older adults’ experiences of diabetes, and how they manage their diabetes with new technological devices. However, little research has focused on their emotional experiences of medical treatment technology, such as mobile applications, tablets, and websites for geriatric diabetes. This study will address both elderly people's experiences and reactions to devices and their children's awareness of diabetes. It aims to find out how to improve the diabetes treatment and create a systematic diabetes mobile application that combines self-initiated and assisted care together.Dissertation/ThesisMasters Thesis Design 201

Intelligent Remote Monitoring and Management system for Type1 Diabetes

The work presented in this thesis focuses on developing a telemedicine system for better management of type1 diabetes in children and teenagers. The research and development of the system is motivated by the inadequate communication in the current system of management of the disease, which results in non-compliance of patients following the regimen. This non-compliance generally results in uncontrolled blood glucose levels, which can result in hypoglycaemia, hyperglycaemia and later life health complications. This further results in an increase in health care costs. In this context, the thesis presents a novel end-to-end, low cost telemedicine system, WithCare+, developed in close collaboration between the University of Sheffield (Electronics & Electrical Engineering) and Sheffield Children’s Hospital. The system was developed to address the challenges of implementing modern telemedicine in type 1 diabetic care with particular relevance to National Health Service children’s clinics in the United Kingdom, by adopting a holistic care driven approach (involving all stakeholders) based on specific key enabler technologies such as low cost and reconfigurable design. However, one of the major issues with current telemedicine system is non-compliance of the patients due to invasive procedure of the glucose measurement which could be clearly addressed by non-invasive method of glucose measurement. Hence, the thesis also makes a contribution towards non-invasive glucose measurement using Near Infrared spectroscopy in terms of addressing the calibration challenge; two methods are proposed to improve the calibration of the Near Infrared instrument. The first method combines locally weighted regression and partial least square regression and the second method combines digital band pass filtering with support vector regression. The efficacy of the proposed methods is validated in experiments carried out in a non-controlled environment and the results obtained demonstrate that the proposed methods improved the performance of the calibration model in comparison to traditional calibration techniques such as Principal Component Regression and Partial Least Squares regression

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

A Proposed Method to Identify the Occurrence of Diabetes in Human Body using Machine Learning Technique

Advanced machine-learning techniques are often used for reasoning-based diagnosis and advanced prediction system within the healthcare industry. The methods and algorithms are based on the historical clinical data and factbased Medicare evaluation. Diabetes is a global problem. Each year people are developing diabetes and due to diabetes, a lot of people are going for organ amputation. According to the World Health Organization (WHO), there is a sharp rise in number of people developing diabetes. In 1980, it was estimated that 180 million people with diabetes worldwide. This number has risen from 108 million to 422 million in 2014. WHO also reported that 1.6 million deaths in 2016 due to diabetes. Diabetes occurs due to insufficient production of insulin from pancreas. Several research show that unhealthy diet, smoking, less exercise, Body Mass Index (BMI) are the primary cause of diabetes. This paper shows the use of machine learning that can identify a patient of being diabetic or non-diabetic based on previous clinical data. In this article, a method is shown to analyze and compare the relationship between different clinical parameters such as age, BMI, Diet-chart, systolic Blood Pressure etc. After evaluating all the factors this research work successfully combined all the related factors in a single mathematical equation which is very effective to analyze the risk percentage and risk evaluation based on given input parameters by the participants or users

Contributions to interoperability, scalability and formalization of personal health systems

The ageing of the world's population combined with unhealthy lifestyles are contributing to a major prevalence of chronic diseases. This scenario poses the challenge of providing good healthcare services to that people affected by chronic illnesses, but without increasing its costs. A prominent way to face this challenge is through pervasive healthcare. Research in pervasive healthcare tries to shift the current centralized healthcare delivery model focused on the doctors, to a more distributed model focused on the patients. In this context Personal Health Systems (PHSs) consists on approaching sampling technologies into the hands of the patients, without disturbing its activities of the daily life, to monitor patient's physiological parameters and providing feedback on their state. The use of PHSs involves the patients in the management of their illness and in their own well being too. The development of PHSs has to face technological issues in order to be accepted by our society. Within them it is important to ensure interoperability between different systems in order to make them work together. Scalability it is also a concern, as their performance must not decrease when increasing the number of users. Another issue is how to formalize the medical knowledge for each patient, as different patients may have different target goals. Security and privacy are a must feature because of the sensitive nature of medical data. Other issues involve the the integration with legacy systems, and the usability of graphical user interfaces in order to encourage old people with the use these technologies. The aim of this PhD thesis is to contribute into the state-of-the-art of PHSs by tackling together different of the above-mentioned challenges. First, to achieve interoperability we use the CDA standard as a format to encode and exchange health data and alerts related with the status of the patient. We show how these documents can be generated automatically through the use of XML templates. Second, we address the scalability by distributing the computations needed to monitor the patients over their devices, rather than performing them in a centralized server. In this context we develop the MAGPIE agent platform, which runs on Android devices, as a framework able to provide intelligence to PHSs, and generate alerts that can be of interest for the patients and the medical doctors. Third, we focus on the formalization of PHSs by providing a tool for the practitioners where they can define, in a graphical way, monitoring rules related with chronic diseases that are integrated with the MAGPIE agent platform. The thesis also explores different ways to share the data collected with PHSs in order to improve the outcomes obtained with the use of this technology. Data is shared between individuals following a Distributed Event-Based System (DEBS) approach, where different people can subscribe to the alerts produced by the patient. Data is also shared between institutions with a network protocol called MOSAIC, and we focus on the security aspects of this protocol. The research in this PhD focuses in the use case of Diabetes Mellitus; and it has been developed in the context of the projects MONDAINE, MAGPIE, COMMODITY12 and TAMESIS.L'envelliment de la població mundial combinat amb uns estils de vida no saludables contribueixen a una major prevalença d'enfermetats cròniques. Aquest escenari presenta el repte de proporcionar uns bons serveis sanitaris a les persones afectades per aquestes enfermetats, sense incrementar-ne els costos. Una solució prometedora a aquest repte és mitjançant l'aplicació del que en anglès s'anomena "pervasive healthcare". L'investigació en aquesta camp tracta de canviar l'actual model centralitzat de serveis sanitaris enfocat en el personal sanitari, per un model de serveis distribuït enfocat en els pacients. En aquest context, els Personal Health Systems (PHSs) consisteixen en posar a l'abast dels pacients les tecnologies de monitorització, i proporcionar-los informació sobre el seu estat. L'ús de PHSs involucra els pacients en la gestió de la seva enfermetat i del seu propi benestar. L'acceptació dels PHSs per part de la societat implica certs reptes tecnològics en el seu desenvolupament. És important garantir la seva interoperabilitat per tal de que puguin treballar conjuntament. La seva escalabilitat també s'ha de tenir en compte, ja que el seu rendiment no s'ha de veure afectat al incrementar-ne el número d'usuaris. Un altre aspecte a considerar és com formalitzar el coneixement mèdic per cada pacient, ja que cada un d'ells pot tenir objectius diferents. La seguretat i privacitat són característiques desitjades degut a la naturalesa sensible de les dades mèdiques. Altres problemàtiques impliquen la integració amb sistemes heretats, i la usabilitat de les interfícies gràfiques per fomentar-ne el seu ús entre les persones grans. L'objectiu d'aquesta tesi és contribuir a l'estat de l'art dels PHSs tractant de manera conjunta varis dels reptes mencionats. Per abordar l'interoperabilitat s'utilitza l'estàndard CDA com a format per codificar les dades mèdiques i alertes relacionades amb el pacient. A més es mostra com aquests documents poden generar-se de forma automàtica mitjançant l' ús de plantilles XML. Per tractar l'escalabilitat es distribueixen les computacions per monitoritzar els pacients entre els seus terminals mòbils, en comptes de realitzar-les en un servidor central. En aquest context es desenvolupa la plataforma d'agents MAGPIE com a framework per proporcionar intelligència als PHSs i generar alertes d'interès per al metge i el pacient. La formalització s'aborda mitjançant una eina que permet als metges definir de manera gràfica regles de monitorització relacionades amb enfermetats cròniques, que a més estan integrades amb la plataforma d'agents MAGPIE. La tesi també explora diferents maneres de compartir les dades recol·lectades amb un PHS, amb l'objectiu de millorar els resultats obtinguts amb aquesta tecnologia. Les dades es comparteixen entre individus seguint un enfoc de sistemes distribuïts basats en events (DEBS), on diferents usuaris poden subscriure's a les alertes produïdes per el pacient. Les dades també es comparteixen entre institucions mitjançant un protocol de xarxa anomenat MOSAIC. A la tesi es desenvolupen els aspectes de seguretat d'aquest protocol. La test es centra en la Diabetis Mellitus com a cas d'ús, i s'ha realitzat en el context dels projectes MONDAINE, MAGPIE, COMMODITY12 i TAMESIS.El envejecimiento de la población mundial combinado con unos estilos de vida no saludables contribuyen a una mayor prevalencia de enfermedades crónicas. Este escenario presenta el reto de proporcionar unos buenos servicios sanitarios a las personas afectadas por estas enfermedades, sin incrementar sus costes. Una solución prometedora a este reto es mediante la aplicación de lo que en inglés se denomina "pervasive healthcare". La investigación en este campo trata de cambiar el actual modelo centralizado de servicios sanitarios enfocado hacia el personal sanitario, por un modelo distribuido enfocado hacia los pacientes. En este contexto, los Personal Health Systems (PHSs) consisten en poner al alcance de los pacientes las tecnologías de monitorización, y proporcionarles información sobre su estado. El uso de PHSs involucra a los pacientes en la gestión de su enfermedad y en su propio bienestar. La aceptación de los PHSs por parte de la sociedad implica ciertos retos tecnológicos en su desarrollo. Es importante garantizar su interoperabilidad para que puedan trabajar conjuntamente. Su escalabilidad también se debe tener en cuenta, ya que su rendimiento no tiene que verse afectado al incrementar su número de usuarios. Otro aspecto a considerar es cómo formalizar el conocimiento médico para cada paciente, ya que cada uno puede tener objetivos distintos. La seguridad y privacidad son características deseadas debido a la naturaleza sensible de los datos médicos. Otras problemáticas implican la integración con sistemas heredados, y la usabilidad de las interfaces gráficas para fomentar su uso entre las personas mayores. El objetivo de esta tesis es contribuir al estado del arte de los PHSs tratando de manera conjunta varios de los retos mencionados. Para abordar la interoperabilidad se usa el estándar CDA como formato para codificar los datos médicos y alertas relacionados con el paciente. Además se muestra como estros documentos pueden generarse de forma automática mediante el uso de plantillas XML. Para tratar la escalabilidad se distribuye la computación para monitorizar a los pacientes en sus terminales móbiles, en lugar de realizarla en un servidor central. En este contexto se desarrolla la plataforma de agentes MAGPIE como framework para proporcionar inteligencia a los PHSs y generar alertas de interés para el médico y el paciente. La formalización se aborda mediante una herramienta que permite a los médicos definir de manera gráfica reglas de monitorización relacionadas con enfermedades crónicas, que ademas están integradas con la plataforma de agentes MAGPIE. La tesis también explora distintas formas de compartir los datos recolectados con un PHS, con el fin de mejorar los resultados obtenidos mediante esta tecnología. Los datos se comparten entre individuos siguiendo un enfoque de sistemas distribuidos basados en eventos (DEBS), donde distintos usuarios pueden suscribirse a las alertas producidas por el paciente. Los datos también se comparten entre instituciones mediante un protocolo dered llamado MOSAIC. En la tesis se desarrollan los aspectos de seguridad de este protocolo. La tesis se centra en la Diabetes Mellitus como caso de uso, y se ha realizado en el contexto de los proyectos MONDAINE, MAGPIE, COMMODITY12 y TAMESIS.Postprint (published version

Implementation of intelligent process automation (IPA) based clinical decision support system for early detection and screening of diabetes : this thesis is presented in partial fulfilment of the requirements for the degree of Master of Information Sciences in Information Technology, School of Natural and Computational Sciences at Massey University Albany, Auckland, New Zealand

Diabetes mellitus has become a leading cause of disease-related deaths in the world. Once an individual is diagnosed with diabetes, a series of processes will be required to keep the blood sugar regular and help avoid hyperglycemia and hypoglycemia. Self-Management of diabetes is complex and involves constant glucose monitoring, diet management, care, support, exercise, and insulin management. These processes are expensive because they require detailed record-keeping of medications, activities, and a timely report to doctors to assist them in making an informed decision that will subsequently help the patient heal. Other challenges include the high cost of treatment, lifestyle changes, education, lack of medication adherence, and treatment plans. Our approach is to adopt the Early screening technique and detect the risk of diabetes unobtrusively. Early screening is a technique that can help detect Type 1, 2 diabetes and achieve preventive care according to the guidelines set by WHO and recommended by the American Diabetes Association (ADA). Unobtrusive systems allow a doctor to screen for diabetes while he is unaware. We followed the Design Science Research model (DSRM) and started by using systematic literature review (SLR) guidelines to search the most popular journals limiting the results tied to studies that discussed the screening and detection of the risk of diabetes. We reviewed the architecture, features, and limitations of the various tools and technologies using the following classification: Continuous Glucose Monitoring Systems (CGMS), Flash Glucose Monitoring Systems (FGMS), and the Unobtrusive Systems. In addition, under the unobtrusive system, we studied the Child Health Improvement through Computer Automation (CHICA) system. While there is evidence that supports its benefits and usefulness, we found some required enhancements from the literature in the areas of decision support systems, data entry automation, and flexible integration with other systems. The artefact built during the development phase is an Intelligent process automation (IPA) system that can be implemented within the health sector for early screening and detection of diabetes unobtrusively. Developing this artefact will allow us to understand the possible issues and challenges of implementing an automation process in a medical institution. We evaluated the artefact using a mix of quantitative and qualitative methods. This method allowed us to answer the research questions and understand the value of automation to medical practitioners. The value includes speed, reduce cost, and error while safeguarding the lives of the medical professional on active duty. The results show that the system can enhance patient-doctor interaction, reduce patient wait time, and optimize the glucose monitoring process. However, there were challenges such as cost of implementation, training of staff, and the increased workload within the system. In addition, potential challenges identified include fear of job loss and aversion to change during implementation within the hospital. This study has also allowed us to understand the integration of robotic process automation with machine learning within the healthcare sector. We hope that this study will contextually position IPA within the technological stack of health care institutions and add to the body of knowledge on this subject