The Virtual Physiological Human: Ten Years After

Biomedical research and clinical practice are struggling to cope with the growing complexity that the progress of health care involves. The most challenging diseases, those with the largest socioeconomic impact (cardiovascular conditions; musculoskeletal conditions; cancer; metabolic, immunity, and neurodegenerative conditions), are all characterized by a complex genotype–phenotype interaction and by a “systemic” nature that poses a challenge to the traditional reductionist approach. In 2005 a small group of researchers discussed how the vision of computational physiology promoted by the Physiome Project could be translated into clinical practice and formally proposed the term Virtual Physiological Human. Our knowledge about these diseases is fragmentary, as it is associated with molecular and cellular processes on the one hand and with tissue and organ phenotype changes (related to clinical symptoms of disease conditions) on the other. The problem could be solved if we could capture all these fragments of knowledge into predictive models and then compose them into hypermodels that help us tame the complexity that such systemic behavior involves. In 2005 this was simply not possible—the necessary methods and technologies were not available. Now, 10 years later, it seems the right time to reflect on the original vision, the results achieved so far, and what remains to be done

Droid Jacket: sistema de monitorização móvel de uma equipa

Mestrado em Engenharia dos Computadores e TelemáticaOs profissionais de emergência lidam no seu quotidiano com situações de perigo, agindo muitas vezes sob pressão, expondo-se a níveis de stress e fadiga por períodos extensos, causando um impacto negativo nas suas vidas e saúde. Neste contexto, a utilização de novas soluções a partir de tecnologias vestíveis, redes de sensores e dispositivos móveis cria a oportunidade de oferecer um acompanhamento mais próximo, com o objectivo de detectar situações de perigo e dar suporte a equipas de profissionais de emergência médica em campo. No entanto, existem muito poucas soluções voltadas para a utilização sinérgica destas tecnologias emergentes que dêem suporte integrado a monitorização de uma equipa. Nesta dissertação propomos uma arquitectura conceptual de software (TeamMonitor) para agregação, análise e disseminação de informação direccionada para a monitorização de equipas na acção. Team Monitor e sustentada na noção de nós de coordenação centrais, que são responsáveis pela recolha de dados de diferentes fontes (ex.: vários profissionais de emergência) e subsequente fluxos de trabalho para análise, incluindo processamento básico de dados (ex.: execução de detectores de alarmes de sinal biológico) e troca eficiente de dados com clientes externos. O nó central dissocia a rede de tecnologias de informação da rede de fornecimento de dados. O suporte é dado pela camada de aquisição de sinal biológico e de análise que nós desenvolvemos, o módulo BIOSal. De modo a ilustrar a viabilidade do TeamMonitor, nós implementámos um sistema como prova do conceito, o Droid Jacket, onde o nó central da TeamMonitor e instanciado num dispositivo móvel com Android. Droid Jacket permite monitorizar até quatro Vital Jacket (uma tecnologia vestível para a monitorização de uma pessoa), fornecendo tanto o suporte para a troca e ficiente dos sinais agregados para clientes externos, como a detec ção precoce de potenciais alarmes a partir do processamento em tempo real dos dados adquiridos. Ao contr ario de outras abordagens comuns, nós consideramos as capacidades de processamento do dispositivo móvel para estação base. Nós implementámos um algoritmo simples de detecção do complexo QRS da onda cardíaca e de arritmias no Droid Jacket, a partir do electrocardiograma adquirido pelas unidades com o Vital Jacket vestido. Droid Jacket demonstra que a incorporação de dispositivos móveis num cenáario de monitorização de uma equipa é uma opção razoável, e o conceito pode ser estendido e adaptado a cenários mais realistas como a monitorização de bombeiros.First responders deal in their daily lives with danger, working under pressure, exposing themselves to stress and fatigue for extended periods, which has a negative impact on their lives and health. In this context, using new solutions based on wearable technologies, sensor networks and mobile devices raises the opportunity to provide closer monitoring, aiming at detecting hazard conditions and supporting rst responder teams in the eld. However, very few solutions exist addressing such synergistic use of these emergent technologies to support integrated team monitoring. In this dissertation we propose a conceptual software architecture (TeamMonitor) for information aggregation, analysis and dissemination towards eld-action teams monitoring. TeamMonitor is supported in the notion of central coordination nodes that are responsible for data aggregation from multiple sources (e.g.: several rst responders professionals) and subsequent analysis work ows, including basic data processing (e.g.: running biosignal alarms detectors) and data stream relay to external clients. The central node decouples the IT network from the data providers network. This support is provided by a biosignal acquisition and analysis framework we developed, the BIOSal module. To illustrate TeamMonitor feasibility, we implemented a proof-ofconcept application, the DroidJacket, in which the TeamMonitor central node is instantiated in an Android mobile device. DroidJacket is able to monitor up to four VitalJacket R devices (a wearable garment for individual monitoring) providing both the support to relay the aggregated signals data to remote clients and an early detection of potential alarms based on real-time processing of the acquired data. Unlike other common approaches, we rely on the mobile device processing capabilities for the base-station. We implemented a basic algorithm for heart wave QRS complex and arrhythmia detection in DroidJacket, using the electrocardiogram acquired from the VitalJacket units. DroidJacket demonstrates that incorporating mobile devices in the team monitoring scenario is a reasonable option nowadays and the concept can be extended and adapted to more realistic scenarios like re ghter monitoring

A Methodological Framework for the Integrated Design of Decision-Intensive Care Pathways\u2014an Application to the Management of COPD Patients

Healthcare processes are by nature complex, mostly due to their multi-disciplinary character that requires continuous coordination between care providers. They encompass both organizational and clinical tasks, the latter ones driven by med- ical knowledge, which is inherently incomplete and distributed among people having different expertise and roles. Care pathways refer to planning and coordination of care processes related to specific groups of patients in a given setting. The goal in defining and following care pathways is to improve the quality of care in terms of patient satisfaction, costs reduction, and medical outcome. Thus, care pathways are a promising methodological tool for standardizing care and decision-making. Business process management techniques can successfully be used for representing organiza- tional aspects of care pathways in a standard, readable, and accessible way, while supporting process development, analysis, and re-engineering. In this paper, we intro- duce a methodological framework that fosters the integrated design, implementation, and enactment of care processes and related decisions, while considering proper rep- resentation and management of organizational and clinical information. We focus here and discuss in detail the design phase, which encompasses the simulation of care pathways. We show how business process model and notation (BPMN) and decision model and notation (DMN) can be combined for supporting intertwined aspects of decision-intensive care pathways. As a proof-of-concept, the proposed methodology has been applied to design care pathways related to chronic obstructive pulmonary disease (COPD) in the region of Veneto, in Italy

The Application of Computer Techniques to ECG Interpretation

This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S

Employment of artificial intelligence mechanisms for e-Health systems in order to obtain vital signs and detect diseases from medical images improving the processes of online consultations and diagnosis

Nowadays e-Health web applications allow doctors to access different types of features, such as knowing which medication the patient has consumed or performing online consultations. Internet systems for healthcare can be improved by using artificial intelligence mechanisms for the process of detecting diseases and obtaining biological data, allowing medical professionals to have important information that facilitates the diagnosis process and the choice of the correct treatment for each particular person. The proposed research work aims to present an innovative approach when compared to traditional platforms, by providing online vital signs in real time, access to a web stethoscope, to a medical image uploader that predicts if a certain disease is present, through deep learning methods, and also allows the visualization of all historical data of a patient. This dissertation has the objective of defending the concept of online consultations, providing complementary functionalities to the traditional methods for performing medical diagnoses through the use of software engineering practices. The process of obtaining vital signs was done via artificial intelligence using a computer camera as sensor. This methodology requires that the user is at a state of rest during the measurements. This investigation led to the conclusion that, in the future, many medical processes will most likely be done online, where this practice is considered extremely helpful for the analysis and treatment of contagious diseases, or cases that require constant monitoring.No quotidiano, as aplicações Web e-Saúde permitem aos médicos acesso a diferentes tipos de funcionalidades, como saber qual a medicação que o doente consumiu ou a realização de consultas online. Os sistemas via internet para a saúde podem ser melhorados, utilizando mecanismos de inteligência artificial para os processos de deteção de doenças e de obtenção de dados biológicos, permitindo que os médicos tenham informações importantes que facilitam o processo de diagnóstico ou a escolha do tratamento correto para um determinado utente. O trabalho de investigação proposto pretende apresentar uma abordagem inovadora na comparação com as plataformas tradicionais, ao disponibilizar sinais vitais online em tempo real, acesso a um estetoscópio web, a um uploader de imagens médicas que prevê se uma determinada doença está presente, através de métodos de aprendizagem profunda, bem como permite visualizar todos os dados históricos de um paciente. Esta dissertação visa defender o conceito de consultas virtuais, providenciando funcionalidades complementares aos processos tradicionais de realização de um diagnóstico médico, através da utilização de práticas de engenharia de software. O processo de obtenção de sinais vitais foi feito através de inteligência artificial para visão computacional utilizando uma câmara de computador. Esta metodologia requer que o utilizador esteja em estado de repouso durante a obtenção dos dados medidos. Esta investigação permitiu concluir que, no futuro, muitos processos médicos atuais provavelmente serão feitos online, sendo esta prática considerada extremamente útil na análise e tratamento de doenças contagiosas, ou de casos que requerem acompanhamento constante