The INCA System: A Further Step Towards a Telemedical Artificial Pancreas

Biomedical engineering research efforts have accomplished another level of a ldquotechnological solutionrdquo for diabetes: an artificial pancreas to be used by patients and supervised by healthcare professionals at any time and place. Reliability of continuous glucose monitoring, availability of real-time programmable insulin pumps, and validation of safe and efficient control algorithms are critical components for achieving that goal. Nevertheless, the development and integration of these new technologies within a telemedicine system can be the basis of a future artificial pancreas. This paper introduces the concept, design, and evaluation of the ldquointelligent control assistant for diabetes, INCArdquo system. INCA is a personal digital assistant (PDA)-based personal smart assistant to provide patients with closed-loop control strategies (personal and remote loop), based on a real-time continuous glucose sensor (Guardian RT, Medtronic), an insulin pump (D-TRON, Disetronic Medical Systems), and a mobile general packet radio service (GPRS)-based telemedicine communication system. Patient therapeutic decision making is supervised by doctors through a multiaccess telemedicine central server that provides to diabetics and doctors a Web-based access to continuous glucose monitoring and insulin infusion data. The INCA system has been technically and clinically evaluated in two randomized and crossover clinical trials showing an improvement on glycaemic control of diabetic patients

Type 1 diabetes

Type 1 diabetes is a chronic autoimmune disease characterised by insulin deficiency and resultant hyperglycaemia. Knowledge of type 1 diabetes has rapidly increased over the past 25 years, resulting in a broad understanding about many aspects of the disease, including its genetics, epidemiology, immune and β-cell phenotypes, and disease burden. Interventions to preserve β cells have been tested, and several methods to improve clinical disease management have been assessed. However, wide gaps still exist in our understanding of type 1 diabetes and our ability to standardise clinical care and decrease disease-associated complications and burden. This Seminar gives an overview of the current understanding of the disease and potential future directions for research and care

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

With the advent of the IoT, AI, ML, and DL algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in \ac{FL} have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable \ac{FL} models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of \ac{FL}, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifying open problems and future research directions.Comment: Accepted at IEEE Internet of Things Journa

Smart Sensors and Virtual Physiology Human Approach as a Basis of Personalized Therapies in Diabetes Mellitus

Diabetes mellitus (DM) has a growing incidence and prevalence in modern societies, pushed by the aging and change of life styles. Despite the huge resources dedicated to improve their quality of life, mortality and morbidity rates, these are still very poor. In this work, DM pathology is revised from clinical and metabolic points of view, as well as mathematical models related to DM, with the aim of justifying an evolution of DM therapies towards the correction of the physiological metabolic loops involved. We analyze the reliability of mathematical models, under the perspective of virtual physiological human (VPH) initiatives, for generating and integrating customized knowledge about patients, which is needed for that evolution. Wearable smart sensors play a key role in this frame, as they provide patient’s information to the models

A dual mode adaptive basal-bolus advisor based on reinforcement learning

Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are commonly used by type 1 diabetes (T1D) patients to measure glucose concentrations. The proposed adaptive basal-bolus algorithm (ABBA) supports inputs from either SMBG or CGM devices to provide personalised suggestions for the daily basal rate and prandial insulin doses on the basis of the patients' glucose level on the previous day. The ABBA is based on reinforcement learning (RL), a type of artificial intelligence, and was validated in silico with an FDA-accepted population of 100 adults under different realistic scenarios lasting three simulated months. The scenarios involve three main meals and one bedtime snack per day, along with different variabilities and uncertainties for insulin sensitivity, mealtime, carbohydrate amount, and glucose measurement time. The results indicate that the proposed approach achieves comparable performance with CGM or SMBG as input signals, without influencing the total daily insulin dose. The results are a promising indication that AI algorithmic approaches can provide personalised adaptive insulin optimisation and achieve glucose control - independently of the type of glucose monitoring technology.Comment: 9 pages, 8 figures, accepted by Journal of Biomedical and Health Informatics in December 201

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

Principles and Applications of Data Science

Data science is an emerging multidisciplinary field which lies at the intersection of computer science, statistics, and mathematics, with different applications and related to data mining, deep learning, and big data. This Special Issue on “Principles and Applications of Data Science” focuses on the latest developments in the theories, techniques, and applications of data science. The topics include data cleansing, data mining, machine learning, deep learning, and the applications of medical and healthcare, as well as social media