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Clinical characteristics of COVID-19 patients have been mostly described in hospitalised patients, yet most are managed in an outpatient setting. The COVID-19 pandemic transformed healthcare delivery models and accelerated the implementation and adoption of telemedicine solutions. We employed a modular remote monitoring system with multi-modal data collection, aggregation, and analytics features to monitor mild COVID-19 patients and report their characteristics and symptoms. At enrolment, the patients were equipped with wearables, which were associated with their accounts, provided the respective in-system consents, and, in parallel, reported the demographics and patient characteristics. The patients monitored their vitals and symptoms daily during a 14-day monitoring period. Vital signs were entered either manually or automatically through wearables. We enrolled 162 patients from February to May 2022. The median age was 51 (42–60) years; 44% were male, 22% had at least one comorbidity, and 73.5% were fully vaccinated. The vitals of the patients were within normal range throughout the monitoring period. Thirteen patients were asymptomatic, while the rest had at least one symptom for a median of 11 (7–16) days. Fatigue was the most common symptom, followed by fever and cough. Loss of taste and smell was the longest-lasting symptom. Age positively correlated with the duration of fatigue, anorexia, and low-grade fever. Comorbidities, the number of administered doses, the days since the last dose, and the days since the positive test did not seem to affect the number of sick days or symptomatology. The i-COVID platform allowed us to provide remote monitoring and reporting of COVID-19 outpatients. We were able to report their clinical characteristics while simultaneously helping reduce the spread of the virus through hospitals by minimising hospital visits. The monitoring platform also offered advanced knowledge extraction and analytic capabilities to detect health condition deterioration and automatically trigger personalised support workflows. [ABSTRACT FROM AUTHOR]

The objective of the Health Bot is to analyze and elaborate on the implementation of an intelligent system that can support telemedicine services using natural language processing (NLP) technology. Health Bot is a robust, modularized, and user-friendly platform that aims to improve the patient's interactions with the health care system. Using NLP and speech recognition algorithms, the platform can analyze and classify free text and voice input data to symptoms. The classified data are used in the machine learning (ML) training process of the artificial intelligence (AI) models in order to predict the probability of the patient suffering from a specific disease and get alerted in case of disorders. The Health Bot is acting as a medical virtual agent in order to collect any information needed in the sense of a medical interview and provide medical assessments, close appointments with the doctors, and monitor/record the patient's health condition. In this paper, two models have been trained for the Covid-19 and heart disease cases with 98.3% and 82% accuracy, respectively. [ABSTRACT FROM AUTHOR]

Electronic health record (EHR) systems improve health care services by allowing the combination of health data with clinical decision support features and clinical image analyses. This study presents a modular and distributed platform that is able to integrate and accommodate heterogeneous, multidimensional (omics, histological images and clinical) data for the multi-angled portrayal and management of skin cancer patients. The proposed design offers a layered analytical framework as an expansion of current EHR systems, which can integrate high-volume molecular -omics data, imaging data, as well as relevant clinical observations. We present a case study in the field of dermatology, where we attempt to combine the multilayered information for the early detection and characterization of melanoma. The specific architecture aspires to lower the barrier for the introduction of personalized therapeutic approaches, towards precision medicine. The paper describes the technical issues of implementation, along with an initial evaluation of the system and discussion. [ABSTRACT FROM AUTHOR]

The proper management of patient data and their accessibility are still remaining issues that prevent the full deployment and usage of pervasive healthcare applications. This paper presents an integrated health monitoring system based on mobile pervasive technologies. The system utilizes Cloud Computing for providing robust and scalable resources for sensor data acquisition, management and communication with external applications like health information systems. A prototype has been developed using both mobile and wearable sensors for demonstrating the usability of the proposed platform. Initial results regarding the performance of the system, the efficiency in data management and user acceptability have been quite promising. [ABSTRACT FROM AUTHOR]

Future Internet (FI) technologies are introducing new ways of networking and cognitive data delivery. In this paper, the potential of FI-based architectures for enabling the context-aware content adaptation and specialized delivery of health related information in assistive environments is investigated. The proposed system utilizes the publish/subscribe internetworking (PSI) architecture, an information-oriented architecture built for the FI using the so-called publish/subscribe paradigm. Information is brought at the center of the approach, providing several advantages: flexibility, seamless information morphing and exploitation of context, access control, and security in general. In addition to an overview of the approach and its characteristics, this work also presents the implementation of a subset of an assistive environment, using Blackadder, PSI's prototype, and illustrates its potential with an emergency service scenario for the assistive healthcare domain. [ABSTRACT FROM AUTHOR]

The present paper studies the prospective and the performance of a forthcoming high-speed third generation (3.5G) networking technology, called enhanced uplink, for delivering mobile health (m-health) applications. The performance of 3.5G networks is a critical factor for successful development of m-health services perceived by end users. In this paper, we propose a methodology for performance assessment based on the joint uplink transmission of voice, real-time video, biological data (such as electrocardiogram, vital signals, and heart sounds), and healthcare records file transfer. Various scenarios were concerned in terms of real-time, nonreal-time, and emergency applications in random locations, where no other system but 3.5G is available. The accomplishment of quality of service (QoS) was explored through a step-by-step improvement of enhanced uplink system's parameters, attributing the network system for the best performance in the context of the desired m-health services. [ABSTRACT FROM AUTHOR]

Two-way satellite broadband communication technologies, such as the Digital Video Broadcasting with Return Channel via Satellite (DVB-RCS) technology, endeavour to offer attractive wide-area broadband connectivity for telemedicine applications, taking into consideration the available data rates, Quality of Service (QoS) provision, survivability, flexibility and operational costs, even in remote areas and isolated regions where the terrestrial technologies suffer. This paper describes a wide-area tele-medicine platform, specially suited for homecare services, based on the DVB-RCS and Wi-Fi communication technologies. The presented platform combines medical data acquisition and transfer, patient remote monitoring and teleconference services. Possible operational scenarios concerning this platform and experimental results regarding tele-monitoring, videoconference and medical data transfer are also provided and discussed in the paper. [ABSTRACT FROM AUTHOR]

Recent developments in networking and computing technologies and the expansion of the electronic health record system have enabled the possibility of online collaboration between geographically distributed medical personnel. In this context, the paper presents a Web-based application, which implements a collaborative working environment for physicians by enabling the peer-to-peer exchange of electronic health records. The paper treats technological issues such as Video, Audio and Message Communication, Workspace Management, Distributed Medical Data Management and exchange, while it emphasizes on the Security issues arisen, due to the sensitive and private nature of the medical information. In the paper, we present initial results from the system in practice and measurements regarding transmission times and bandwidth requirements. A wavelet based image compression scheme is also introduced for reducing network delays. A number of physicians were asked to use the platform for testing purposes and for measuring user acceptance. The system was considered by them to be very useful, as they found that the platform simulated very well the personal contact between them and their colleagues during medical meetings. [ABSTRACT FROM AUTHOR]