A comparison of the performance and scalability of relational and document-based web-systems for large scale applications in a rehabilitation context

Background: The Virtual Rehabilitation Environment (VRE) provides patients of long term neurological conditions with a platform to review their previous physiotherapy sessions, as well as see their goals and any treatments or exercises that their clinician has set for them to practice before their next session. Objective: The initial application implemented 21 of the 27 core features using the Microsoft ASP.NET MVC stack. However, the two core, non-functional requirements were negated from the project due to lack of experience and strict time constraints. This project aimed to investigate whether the application would be more suited to a non-relational solution. Method: The application was re-written using the MEAN stack (MongoDB, ExpressJS, AngularJS, NodeJS), an open source, fully JavaScript stack and then performance tests were carried out to compare the two applications. A scalability review was also conducted to assess the benefits and drawbacks of each technology in this aspect. Results: The investigation proved that the non-relational solution was much more efficient and performed faster. However, the choice of database was only a small part of the increase in efficiency and it was an all-round better design that gave the new application its performance upper hand. Conclusion: A proposal for a new application design is given that follows the microservice architecture used by companies such as Amazon and Netflix. The application is to be split up into four parts; database, client application, server application and content delivery network. These four, independently scalable and manageable services offer the greatest flexibility for future development at the low costs necessary for a start-up.Comment: Unpublished MSc thesi

A software to manage rehabilitation sessions with a robotic walker

Dissertação de mestrado integrado em Informatics EngineeringCerebellar ataxia arises from damage or dysfunction that affects the cerebellum and its pathways. As a result, the motor abilities of individuals with this condition become weakened. Robotics-assisted therapy is still an emerging area, but it has several advantages that could boost the rehabilitation of these individuals. Considering this problematic, WALKit Smart Walker is being developed. Its main purpose is to improve the treatment of ataxic patients through intelligent and multidisciplinary rehabilitation sessions. Thus, it is equipped with several sensors that provide monitoring capabilities through a continuous evaluation of the end-user gait and posture. A vast amount of data is acquired during each session by the walker sensors. For health professionals to analyse this data and have feedback on the patient’s status throughout therapy, tools are needed to control, manage, and monitor sessions in a clear, practical and intuitive way. Therefore, the main goal of this dissertation is centred on implementing an effective way to store the acquired data, along with the development of software that satisfies these requirements. To address these goals, a polyglot persistence database system, composed of a relational and a non-relational database, was implemented to store the required data while maintaining efficiency. Furthermore, a web application was developed to provide, not only to health professionals, but also to patients themselves, the management of the rehabilitation sessions with the walker. The application provides an individual and temporal analysis of the sessions through interactive graphics adapted to each patient. Additionally, it allows the management of the several patients who are/were in treatment and the addition of clinical ratting scales, which are useful to assess their motor condition and adapt therapies as needed. In this way, professionals can have a better perception of the patient’s condition, and can show patients their evolution, possibly contributing to increase their motivation in therapy. Moreover, in the context of this dissertation, the embedded software of WALKit SmartW, which allows the therapy configuration, was optimized. This software had no security mechanisms, thus the main goal was on the implementation of techniques capable of making the software secure. Additionally, other functionalities such as feedback alerts, were added to the existing application. Throughout the development of this project, it was possible to have continuous feedback from health professionals of the Hospital of Braga. Usability tests and questionnaires were also applied, and the results were very promising, enhancing the need for a system with these characteristics. Professionals claimed the system may help in analysing the patient clinical status in an intuitive form while keeping them motivated during treatments.A ataxia cerebelar surge a partir de danos ou disfunções que afetam o cerebelo e as suas vias. Como resultado, as capacidades motoras dos indivíduos que possuem esta condição ficam fragilizadas. A terapia assistida por robôs é ainda uma área em desenvolvimento, no entanto apresenta diversas vantagens que poderão agilizar os tratamentos destes indivíduos. Atendendo a esta problemática, o WALKit SmartW encontra-se a ser desenvolvido. O seu principal propósito é auxiliar os tratamentos de pacientes ataxicos através de sessões de reabilitação inteligentes e multidisciplinares. Para tal, é composto por um conjunto de sensores que fornecem uma monitorização e avaliação contínua da marcha e da postura do utilizador. Uma grande quantidade de dados é adquirida ao longo de cada sessão através dos sensores. De forma a que os profissionais de saúde analisem estes dados e tenham feedback do estado do paciente ao longo da terapia, são necessárias ferramentas que permitam controlar, gerir e monitorizar as sessões, de forma clara, prática e intuitiva. O principal objetivo desta dissertação centra-se na implementação de uma estratégia eficiente para armazenar os dados, juntamente com o desenvolvimento de um software que satisfaça estes requisitos. Para cumprir estes objetivos, um sistema de base de dados com persistência poliglota, composto por uma base de dados relacional e uma não relacional, foi implementado para armazenar os dados mantendo a eficiência. Além disso, uma aplicação web foi desenvolvida para proporcionar, não só aos profissionais de saúde, como também aos próprios pacientes, a gestão das sessões de reabilitação com o andarilho. A aplicação disponibiliza uma análise individual e temporal das sessões através de gráficos interativos adaptados a cada paciente. Adicionalmente, possibilita também a gestão dos diversos pacientes que estão/estiveram em tratamento, e a adição de escalas de classificação clínica, que são úteis para avaliar a condição motora e adaptar as terapias conforme necessário. Desta forma, os profissionais conseguem ter uma melhor perceção acerca do estado do paciente, e os pacientes podem ver a sua evolução, contribuindo para aumentar a motivação na terapia. Ainda no contexto desta dissertação, otimizou-se a aplicação embebida no software do andarilho WALKit, que permite as configurações da terapia. O software era isento de qualquer mecanismo de segurança, pelo que o maior foco centrou-se na aplicação de técnicas capazes de o tornar seguro. Adicionalmente, outras funcionalidades, como alertas e configurações de algoritmos, foram adicionadas à aplicação existente. Ao longo do desenvolvimento deste projeto, foi possível obter o feedback contínuo de profissionais de saúde do Hospital de Braga. Testes e questionários de usabilidade foram também aplicados, e os resusltados foram bastante promissores, reforçando a necessidade de um sistema com estas características. Os profissionais afirmaram que o sistema irá ajudar a analisar o estado do paciente de forma intuitiva, mantendo-o motivado durante os tratamentos

An evaluation of the performance of a NoSQL document database in a simulation of a large scale Electronic Health Record (EHR) system

Electronic Healthcare Record (EHR) systems can provide significant benefits by improving the effectiveness of healthcare systems. Research and industry projects focusing on storing healthcare information in NoSQL databases has been triggered by practical experience demonstrating that a relational database approach to managing healthcare records has become a bottleneck. Previous studies show that NoSQL databases based on consistency, availability and partition tolerance (CAP) theorem have significant advantages over relational databases such as easy and automatic scaling, better performance and high availability. However, there is limited empirical research that has evaluated the suitability of NoSQL databases for managing EHRs. This research addressed this identified research problem and gap in the literature by investigating the following general research: How can a simulation of a large EHR system be developed so that the performance of NoSQL document databases comparative to relational databases can be evaluated? Using a Design Science approach informed by a pragmatic worldview, a number of IT artefacts were developed to enable an evaluation of performance of a NoSQL document oriented database comparative to a relational database in a simulation of a large scale EHR system. These were healthcare data models (NoSQL document database, relational database) for the Australian Healthcare context, a random healthcare data generator and a prototype EHR system. The performance of a NoSQL document database (Couchbase) was evaluated comparative to a relational database (MySQL) in terms database operations (insert, update, delete of EHRs), scalability, EHR sharing and data analysis (complex querying) capabilities in a simulation of a large scale EHR system, constructed in the cloud environment of Amazon Web Services (AWS). Test scenarios consisted of a number of different configurations ranging from 1, 2, 4, 8 and 16 nodes for 1Million, 10 Million, 100 Million and 500 Million records to simulate database operations in a large scale and distributed EHR system environment. The Couchbase NoSQL document database was found to perform significantly better than the MySQL relational database in most of the test cases in terms of database operations -insert, update, delete of EHRs, scalability and EHR sharing. However, the MySQL relational database was found to perform significantly better than the Couchbase NoSQL document database for the complex query test that demonstrates basic analysis capabilities. Furthermore, the Couchbase NoSQL document database used significantly more disk space than the MySQL relational database to store the same number of EHRs. This research made a number of important contributions to knowledge, theory and practice. The main theoretical contribution to design theory was the design and evaluation of a prototype EHR system for simulating database management operations in a large scale EHR system environment. The prototype EHR system was underpinned by the development of two data models with data structures designed for a NoSQL document database and a relational database and a random healthcare data generator which were based on Australian Healthcare data characteristics and statistics. The design of a data model for EHRs for a NoSQL document database using an aggregated document modelling approach provided an important contribution to data modelling theory for NoSQL document databases using de-normalisation and document aggregation. The design of a random healthcare data generator was another important contribution to design theory and was based on a data distribution algorithm (multinomial distribution and probability theory) informed by National Health Data Dictionary and published Australian Healthcare statistics. The prototype EHR system allowed this study to demonstrate through a simulated performance evaluation that a NoSQL document database has significant and proven performance advantages over relational databases in most of the database management test cases. Hence this study demonstrated the utility and efficacy of a NoSQL document database in the simulation of a large scale EHR system. This research has made a number of important contributions to practice. Foremost is that the IT artefacts (namely, a data model for storing EHRs in a NoSQL document database, a random healthcare data generator and a prototype EHR system) developed and evaluated in this research can be readily adopted by practitioners. Another important practical contribution of this research is that it is based on the open source availability of NoSQL database and relational database alternatives. Hence, this research can provide a sound basis for lower-income countries as well higher-income countries to establish their own cost-effective national EHR systems without the restrictions, limitations, complexity or complications of similar proprietary relational database systems

METADATA MANAGEMENT FOR CLINICAL DATA INTEGRATION

Clinical data have been continuously collected and growing with the wide adoption of electronic health records (EHR). Clinical data have provided the foundation to facilitate state-of-art researches such as artificial intelligence in medicine. At the same time, it has become a challenge to integrate, access, and explore study-level patient data from large volumes of data from heterogeneous databases. Effective, fine-grained, cross-cohort data exploration, and semantically enabled approaches and systems are needed. To build semantically enabled systems, we need to leverage existing terminology systems and ontologies. Numerous ontologies have been developed recently and they play an important role in semantically enabled applications. Because they contain valuable codified knowledge, the management of these ontologies, as metadata, also requires systematic approaches. Moreover, in most clinical settings, patient data are collected with the help of a data dictionary. Knowledge of the relationships between an ontology and a related data dictionary is important for semantic interoperability. Such relationships are represented and maintained by mappings. Mappings store how data source elements and domain ontology concepts are linked, as well as how domain ontology concepts are linked between different ontologies. While mappings are crucial to the maintenance of relationships between an ontology and a related data dictionary, they are commonly captured by CSV files with limits capabilities for sharing, tracking, and visualization. The management of mappings requires an innovative, interactive, and collaborative approach. Metadata management servers to organize data that describes other data. In computer science and information science, ontology is the metadata consisting of the representation, naming, and definition of the hierarchies, properties, and relations between concepts. A structural, scalable, and computer understandable way for metadata management is critical to developing systems with the fine-grained data exploration capabilities. This dissertation presents a systematic approach called MetaSphere using metadata and ontologies to support the management and integration of clinical research data through our ontology-based metadata management system for multiple domains. MetaSphere is a general framework that aims to manage specific domain metadata, provide fine-grained data exploration interface, and store patient data in data warehouses. Moreover, MetaSphere provides a dedicated mapping interface called Interactive Mapping Interface (IMI) to map the data dictionary to well-recognized and standardized ontologies. MetaSphere has been applied to three domains successfully, sleep domain (X-search), pressure ulcer injuries and deep tissue pressure (SCIPUDSphere), and cancer. Specifically, MetaSphere stores domain ontology structurally in databases. Patient data in the corresponding domains are also stored in databases as data warehouses. MetaSphere provides a powerful query interface to enable interaction between human and actual patient data. Query interface is a mechanism allowing researchers to compose complex queries to pinpoint specific cohort over a large amount of patient data. The MetaSphere framework has been instantiated into three domains successfully and the detailed results are as below. X-search is publicly available at https://www.x-search.net with nine sleep domain datasets consisting of over 26,000 unique subjects. The canonical data dictionary contains over 900 common data elements across the datasets. X-search has received over 1800 cross-cohort queries by users from 16 countries. SCIPUDSphere has integrated a total number of 268,562 records containing 282 ICD9 codes related to pressure ulcer injuries among 36,626 individuals with spinal cord injuries. IMI is publicly available at http://epi-tome.com/. Using IMI, we have successfully mapped the North American Association of Central Cancer Registries (NAACCR) data dictionary to the National Cancer Institute Thesaurus (NCIt) concepts

Wiki-health: from quantified self to self-understanding

Today, healthcare providers are experiencing explosive growth in data, and medical imaging represents a significant portion of that data. Meanwhile, the pervasive use of mobile phones and the rising adoption of sensing devices, enabling people to collect data independently at any time or place is leading to a torrent of sensor data. The scale and richness of the sensor data currently being collected and analysed is rapidly growing. The key challenges that we will be facing are how to effectively manage and make use of this abundance of easily-generated and diverse health data. This thesis investigates the challenges posed by the explosive growth of available healthcare data and proposes a number of potential solutions to the problem. As a result, a big data service platform, named Wiki-Health, is presented to provide a unified solution for collecting, storing, tagging, retrieving, searching and analysing personal health sensor data. Additionally, it allows users to reuse and remix data, along with analysis results and analysis models, to make health-related knowledge discovery more available to individual users on a massive scale. To tackle the challenge of efficiently managing the high volume and diversity of big data, Wiki-Health introduces a hybrid data storage approach capable of storing structured, semi-structured and unstructured sensor data and sensor metadata separately. A multi-tier cloud storage system—CACSS has been developed and serves as a component for the Wiki-Health platform, allowing it to manage the storage of unstructured data and semi-structured data, such as medical imaging files. CACSS has enabled comprehensive features such as global data de-duplication, performance-awareness and data caching services. The design of such a hybrid approach allows Wiki-Health to potentially handle heterogeneous formats of sensor data. To evaluate the proposed approach, we have developed an ECG-based health monitoring service and a virtual sensing service on top of the Wiki-Health platform. The two services demonstrate the feasibility and potential of using the Wiki-Health framework to enable better utilisation and comprehension of the vast amounts of sensor data available from different sources, and both show significant potential for real-world applications.Open Acces

EldyIoT: IoT assistive system for elderly

The Internet of Things (IoT) is one of the most promising technologies for the near future. IoT has penetrated many industries such as smart cities, smart homes, smart cars, healthcare, agriculture and manufacturing. In the health sector, the number of use cases has increased as a consequence of the improvement in the quality and effectiveness of the provision of hospital and care services. This work presents a solution to reduce the distance between caregivers and patients in a non-intrusive way, allowing information about the health status and mobility of patients to be available and accessible anywhere, anytime. Consecutively, it will increase the confidence of caregivers in relation to the health status of monitored patients and, on the other hand, it will increase the confidence of patients that their health status is being monitored frequently. Thus, this thesis presents a framework for physical monitoring of elderly people based on smartwatch devices. As an integral part of the solution, we present a cross-platform mobile application and a software framework assembled to support the processes of collecting, processing, storing, displaying and analyzing data.A Internet das Coisas (IoT) é uma das tecnologias mais promissoras para o futuro próximo. A IoT penetrou em muitos setores, como cidades inteligentes, casas inteligentes, carros inteligentes, saúde, agricultura e manufatura. No sector da saúde, o número de casos de uso, tem aumentado como consequência do incremento na qualidade e eficácia na prestação de serviços hospitalares e de cuidados. Este trabalho, apresenta uma solução para reduzir a distância entre cuidadores e pacientes de forma não intrusiva, permitindo que informações sobre o estado de saúde e mobilidade dos pacientes estejam disponíveis e acessíveis em qualquer lugar, e a qualquer hora. Consecutivamente, aumentará a confiança dos cuidadores em relação ao estado de saúde dos pacientes acompanhados e, por outro lado, aumentará a confiança dos pacientes de que o seu estado de saúde está a ser monitorizado com frequência. Assim, esta tese apresenta um framework para monitorização física de idosos baseada em dispositivos smartwatch. Como parte integrante da solução, apresentamos uma aplicação móvel cross-platform e uma estrutura de software montada para dar suporte aos processos de recolher, processamento, armazenamento, exibição e análise de dados

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Computing Network of Diseases and Pharmacological Entities through the Integration of Distributed Literature Mining and Ontology Mapping

The proliferation of -omics (such as, Genomics, Proteomics) and -ology (such as, System Biology, Cell Biology, Pharmacology) have spawned new frontiers of research in drug discovery and personalized medicine. A vast amount (21 million) of published research results are archived in the PubMed and are continually growing in size. To improve the accessibility and utility of such a large number of literatures, it is critical to develop a suit of semantic sensitive technology that is capable of discovering knowledge and can also infer possible new relationships based on statistical co-occurrences of meaningful terms or concepts. In this context, this thesis presents a unified framework to mine a large number of literatures through the integration of latent semantic analysis (LSA) and ontology mapping. In particular, a parameter optimized, robust, scalable, and distributed LSA (DiLSA) technique was designed and implemented on a carefully selected 7.4 million PubMed records related to pharmacology. The DiLSA model was integrated with MeSH to make the model effective and efficient for a specific domain. An optimized multi-gram dictionary was customized by mapping the MeSH to build the DiLSA model. A fully integrated web-based application, called PharmNet, was developed to bridge the gap between biological knowledge and clinical practices. Preliminary analysis using the PharmNet shows an improved performance over global LSA model. A limited expert evaluation was performed to validate the retrieved results and network with biological literatures. A thorough performance evaluation and validation of results is in progress

Enhanced Living Environments

This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area