Mobile Telemedicine for Diabetes Care

Diabetes Mellitus is nowadays one of the most frequent non-contagious diseases in the world and remains a major health problem for the national health care programs. It is well proved that Telemedicine helps diabetic patients controlling their glucose levels, facilitating their day to-day therapy management and the communication with health care personnel. The rapid growth and development of information technologies in the areas of mobile computing and mobile Internet is shaping a new technological scenario of telemedicine and shared care systems. In this chapter we will show one approach to Mobile Telemedicine for Diabetes Care

Remote Screening And Self-Monitoring For Vision Loss Diseases Based On Smartphone Applications

Remote Healthcare Monitoring System (RHMS) represents remote observing of patient’s well-being and providing therapeutic services. Sensors play an essential part in RHMs. They measure the physical parameters and give continuous information to health organizations, doctors. The presence of Smartphones and other portable devices have allowed us to utilize remote healthcare monitoring system for an assortment of structures. Also, Wireless Sensor Network (WSN) advances considered as one of the critical research factor healthcare application for enhancing the standard of living. In this dissertation, I have presented three tiers operating in the remote healthcare monitoring system; the Body Area Network (BAN), the PAN Coordinator and the Back- Medical End System (BMEsys). The three tiers focused on several patients PAN coordinators include the Wireless Sensor Network. The Wireless Sensor Network can be used at the fixed tale-monitor location and periodic measurements. The Personal Digital Assistant (PDA) can be used in patients own home or community setting with continuous measurements and smartphones can be utilized anywhere with full range parameters, and I have provided a meaningful utilization comparison between Wireless Sensor Network, PDA and smartphone in Remote Healthcare Monitoring System (HRMs) architecture design. Evaluate the approaches of the healthcare monitoring system architecture and investigate the use of advanced technologies enabling the patient vital signs and diagnostic medical team in real-time. This dissertation demonstrates that how a Smartphone can be used for medical treatment in the field of Ophthalmology and discussed how a Smartphone and its technology could be used to diagnose loss of eye vision. Most recent smartphones have been equipped with a featured camera with high megapixels and advanced sensors which can be used to record fundus photographs through a slit lamp or record videos from an operating microscope and display images from optical coherence tomography systems and other high-tech devices. The ophthalmologists can share these images and analyze with their colleagues utilizing media sharing applications and make the optimal diagnostic and therapeutic results to diagnose the low vision of patients. At present, three widely used pocket-sized adapters can improve the magnification and lighting of the camera, which enables the smartphones to capture high-quality images of the eye. These are Portable Eye Examination Kit (PEEK), EyeGo, and D-Eye. Peek Adapter consists of a smartphone application and retina adapter which can be clipped onto the device and synchronized with the peek application for sharing and analyzing the images. This adapter can be used by anyone and anywhere in the world to examine eyes. EyeGo is an adapter intended to allow ophthalmologists and healthcare specialists to capture high-quality images of the eye using an ophthalmic lens. D-Eye Adapter is one of the extensively used adapters which yield excellent results. It consists of a portable eye and retinal system that fits onto a smartphone creating a retinal camera for evaluation and screening of the eye. It uses LED lights as a light source and requires no extra power, making it an ideal solution for portable diagnostics. The medical field has widely accepted these adaptors with the smartphones for diagnosing low vision and eye-related infections. In this dissertation, I also provide a meaningful utilization comparison between the smartphone adapters: D-Eye, EyeGo and Portable Eye Examination Kit (PEEK). In this dissertation, I have developed a new App (Remote Healthcare-Monitoring Mobile App) to help patients who have low vision and who are suffering from the diseases which may cause a vision loss. This app is capable of a process, evaluate, interact and store health data which is continuously measured by (Personal Health Monitors). This App can exchange the information directly to the Smartphone users (patients) and the doctor who allows more security and privacy. The idea of the App consists of the following: A Smartphone Application, a Data Collection Center, and Professionals in Ophthalmology. The patient should be registered in the system, for example, (Retina Michigan Center or Glaucoma Michigan Center). After registration, the patient is instructed on how to take photos of his/her eyes correctly, and then use the Smartphone application. The patient takes photos of his/her eyes and sends them to the data collection center, the specialists get access to these data and help in the treatment according to the analysis. Finally, I completed the development of the Mobile app (including the Skype and Viber links), which can help in exchanging the information between the patient and the doctor

Medical Estimating PF Machine Learning and IoT in Melancholy among Diabetic Patients

To break down the frequency and related risk elements of sorrow in patients with type 2 diabetes mellitus in the nearby local area and give logical references to clinical anticipation and treatment of diabetes mellitus with wretchedness. Proposed strategies use AI more than 58 patients with type 2 diabetes mellitus were chosen by efficient inspecting, and pertinent surveys examined segment factors and related clinical variables and misery sub-scale (PHQ) was utilized to assess the level of sadness. Social help scale was utilized to survey the patients. SSRS are utilized to assess individual social help levels and lead measurable investigation. After assessment it's seen that among the 58 patients with type 2 diabetes, the rate of consolidated melancholy was 58%; the age, conjugal status, training level, occupation, family ancestry, term of diabetes, entanglements, work out. The distinction in friendly help was genuinely critical. The impacting elements of type 2 diabetes confounded with gloom incorporate age, conjugal status, training level, occupation, and family ancestry, span of diabetes, presence or nonappearance of confusions, exercise and social help. They have a high gamble of muddled sorrow and influence the improvement of diabetes

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

The doctoral research abstracts Vol:1 2012 / Institute of Graduate Studies, UiTM

Foreword: Congratulations to Institute of Graduate Studies on the 1st issue of The Doctoral Research Abstracts. This inaugural issue consists of 40 abstracts from our PhD graduands receiving their scrolls in the UiTM’s 76th Convocation. This convocation is very significant especially for UiTM since we are celebrating the success of 40 PhD graduands from 12 of the university’s 25 faculties – the largest number ever conferred at any one time. To the 40 doctorates, I would like it to be known that you have most certainly done UiTM proud by journeying through the scholastic path with its endless challenges and impediments, and by persevering right till the very end. Let it remain in your thoughts and hearts that knowledge is Godgiven, and for those of us who have some to spare, never fear to share with those around us, and never be sparing in serving the community and the country, in the name of the Almighty. Dato’ Prof Ir Dr Sahol Hamid Bin Abu Bakar , FASc Vice Chancellor Universiti Teknologi MAR

Making New "New AI" Friends : Designing a Social Robot for Diabetic Children from an Embodied AI Perspective

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Robin is a cognitively and motivationally autonomous affective robot toddler with "robot diabetes" that we have developed to support perceived self-efficacy and emotional wellbeing in children with diabetes by providing them with positive mastery experiences of diabetes management in a playful but realistic and natural interaction context. Underlying the design of Robin is an "Embodied" (formerly also known as "New") Artificial Intelligence approach to robotics. In this paper we discuss the rationale behind the design of Robin to meet the needs of our intended end users (both children and medical staff), and how "New AI" provides a suitable approach to developing a friendly companion that fulfills the therapeutic and affective requirements of our end users beyond other approaches commonly used in assistive robotics and child-robot interaction. Finally, we discuss how our approach permitted our robot to interact with and provide suitable experiences of diabetes management to children with very different social interaction styles.Peer reviewedFinal Published versio

ASSESSMENT OF RISK SCORES FOR THE PREDICTION AND DETECTION OF TYPE 2 DIABETES MELLITUS IN CLINICAL SETTINGS

Health and sociological indicators confirm that life expectancy is increasing, and so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 Diabetes is one of the most common chronic diseases, specially linked to overweight and ages over sixty. As a metabolic disease, Type 2 Diabetes affects multiple organs by causing damage in blood vessels and nervous system at micro and macro scale. Mortality of subjects with diabetes is three times higher than the mortality for subjects with other chronic diseases. On the one hand, the management of diabetes is focused on the maintenance of the blood glucose levels under a threshold by the prescription of anti-diabetic drugs and a combination of healthy food habits and moderate physical activity. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of Type 2 Diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. On the other hand, prospective research has been driven on large groups of population to build risk scores which aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and, to date, none of them has been tested on a population based study. The research study presented in this doctoral thesis strives to use externally validated risk scores for the prediction and detection of Type 2 Diabetes on a population data base in Hospital La Fe (Valencia, Spain). The study hypothesis is that the integration of existing prediction and detection risk scores on Electronic Health Records increases the early-detection of high risk cases. To evaluate this hypothesis three studies on the clinical, user and technology dimensions have been driven to evaluate the extent to which the models and the hospital is ready to exploit such models to identify high risk groups and drive efficient preventive strategies. The findings presented in this thesis suggest that Electronic Health Records are not prepared to massively feed risk models. Some of the evaluated models have shown a good classification performance, which accompanied to the well-acceptance of web-based tools and the acceptable technical performance of the information and communication technology system, suggests that after some work these models can effectively drive a new paradigm of active screening for Type 2 Diabetes.Los indicadores de salud y sociológicos confirman que la esperanza de vida está aumentando, y por lo tanto, los años que los pacientes tienen que vivir con enfermedades crónicas y comorbilidades. Diabetes tipo 2 es una de las enfermedades crónicas más comunes, especialmente relacionadas con el sobrepeso y edades superiores a los sesenta años. Como enfermedad metabólica, la diabetes tipo 2 afecta a múltiples órganos causando daño en los vasos sanguíneos y el sistema nervioso a escala micro y macro. La mortalidad de sujetos con diabetes es tres veces mayor que la mortalidad de sujetos con otras enfermedades crónicas. Por un lado, la estrategia de manejo se centra en el mantenimiento de los niveles de glucosa en sangre bajo un umbral mediante la prescripción de fármacos antidiabéticos y una combinación de hábitos alimentarios saludables y actividad física moderada. Estudios recientes han demostrado la eficacia de nuevas estrategias para retrasar e incluso prevenir la aparición de la diabetes tipo 2 mediante una combinación de estilo de vida activo y saludable en cohortes de sujetos de riesgo medio a alto. Por otro lado, la investigación prospectiva se ha dirigido a grupos de la población para construir modelos de riesgo que pretenden obtener una regla para la clasificación de las personas según las probabilidades de desarrollar la enfermedad. Actualmente hay más de doscientos modelos de riesgo para hacer esta identificación, no obstante la inmensa mayoría no han sido debidamente evaluados en grupos externos y, hasta la fecha, ninguno de ellos ha sido probado en un estudio poblacional. El estudio de investigación presentado en esta tesis doctoral pretende utilizar modelos riesgo validados externamente para la predicción y detección de la Diabetes Tipo 2 en una base de datos poblacional del Hospital La Fe de Valencia (España). La hipótesis del estudio es que la integración de los modelos de riesgo de predicción y detección existentes la práctica clínica aumenta la detección temprana de casos de alto riesgo. Para evaluar esta hipótesis, se han realizado tres estudios sobre las dimensiones clínicas, del usuario y de la tecnología para evaluar hasta qué punto los modelos y el hospital están dispuestos a explotar dichos modelos para identificar grupos de alto riesgo y conducir estrategias preventivas eficaces. Los hallazgos presentados en esta tesis sugieren que los registros de salud electrónicos no están preparados para alimentar masivamente modelos de riesgo. Algunos de los modelos evaluados han demostrado un buen desempeño de clasificación, lo que acompañó a la buena aceptación de herramientas basadas en la web y el desempeño técnico aceptable del sistema de tecnología de información y comunicación, sugiere que después de algún trabajo estos modelos pueden conducir un nuevo paradigma de la detección activa de la Diabetes Tipo 2.Els indicadors sociològics i de salut confirmen un augment en l'esperança de vida, i per tant, dels anys que les persones han de viure amb malalties cròniques i comorbiditats. la diabetis de tipus 2 és una de les malalties cròniques més comunes, especialment relacionades amb l'excés de pes i edats superiors als seixanta anys. Com a malaltia metabòlica, la diabetis de tipus 2 afecta múltiples òrgans causant dany als vasos sanguinis i el sistema nerviós a escala micro i macro. La mortalitat de subjectes amb diabetis és tres vegades superior a la mortalitat de subjectes amb altres malalties cròniques. D'una banda, l'estratègia de maneig se centra en el manteniment dels nivells de glucosa en sang sota un llindar mitjançant la prescripció de fàrmacs antidiabètics i una combinació d'hàbits alimentaris saludables i activitat física moderada. Estudis recents han demostrat l'eficàcia de noves estratègies per a retardar i fins i tot prevenir l'aparició de la diabetis de tipus 2 mitjançant una combinació d'estil de vida actiu i saludable en cohorts de subjectes de risc mitjà a alt. D'altra banda, la investigació prospectiva s'ha dirigit a grups específics de la població per construir models de risc que pretenen obtenir una regla per a la classificació de les persones segons les probabilitats de desenvolupar la malaltia. Actualment hi ha més de dos-cents models de risc per fer aquesta identificació, però la immensa majoria no han estat degudament avaluats en grups externs i, fins ara, cap d'ells ha estat provat en un estudi poblacional. L'estudi d'investigació presentat en aquesta tesi doctoral utilitza models de risc validats externament per a la predicció i detecció de diabetis de tipus 2 en una base de dades poblacional de l'Hospital La Fe de València (Espanya). La hipòtesi de l'estudi és que la integració dels models de risc de predicció i detecció existents la pràctica clínica augmenta la detecció de casos d'alt risc. Per avaluar aquesta hipòtesi, s'han realitzat tres estudis sobre les dimensions clíniques, de l'usuari i de la tecnologia per avaluar fins a quin punt els models i l'hospital estan disposats a explotar aquests models per identificar grups d'alt risc i conduir estratègies preventives. Les troballes presentades sugereixen que els registres de salut electrònics no estan preparats per alimentar massivament models de risc. Alguns dels models avaluats han demostrat una bona classificació, el que va acompanyar a la bona acceptació d'eines basades en el web i el rendiment tècnic acceptable del sistema de tecnologia d'informació i comunicacions implementat. La conclusió es que encara es necesari treball per que aquests models poden conduir un nou paradigma de la detecció activa de la diabetis de tipus 2.Martínez Millana, A. (2017). ASSESSMENT OF RISK SCORES FOR THE PREDICTION AND DETECTION OF TYPE 2 DIABETES MELLITUS IN CLINICAL SETTINGS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86209TESI

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998