2,655 research outputs found

    An ontology for formal representation of medication adherence-related knowledge : case study in breast cancer

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    Indiana University-Purdue University Indianapolis (IUPUI)Medication non-adherence is a major healthcare problem that negatively impacts the health and productivity of individuals and society as a whole. Reasons for medication non-adherence are multi-faced, with no clear-cut solution. Adherence to medication remains a difficult area to study, due to inconsistencies in representing medicationadherence behavior data that poses a challenge to humans and today’s computer technology related to interpreting and synthesizing such complex information. Developing a consistent conceptual framework to medication adherence is needed to facilitate domain understanding, sharing, and communicating, as well as enabling researchers to formally compare the findings of studies in systematic reviews. The goal of this research is to create a common language that bridges human and computer technology by developing a controlled structured vocabulary of medication adherence behavior—“Medication Adherence Behavior Ontology” (MAB-Ontology) using breast cancer as a case study to inform and evaluate the proposed ontology and demonstrating its application to real-world situation. The intention is for MAB-Ontology to be developed against the background of a philosophical analysis of terms, such as belief, and desire to be human, computer-understandable, and interoperable with other systems that support scientific research. The design process for MAB-Ontology carried out using the METHONTOLOGY method incorporated with the Basic Formal Ontology (BFO) principles of best practice. This approach introduces a novel knowledge acquisition step that guides capturing medication-adherence-related data from different knowledge sources, including adherence assessment, adherence determinants, adherence theories, adherence taxonomies, and tacit knowledge source types. These sources were analyzed using a systematic approach that involved some questions applied to all source types to guide data extraction and inform domain conceptualization. A set of intermediate representations involving tables and graphs was used to allow for domain evaluation before implementation. The resulting ontology included 629 classes, 529 individuals, 51 object property, and 2 data property. The intermediate representation was formalized into OWL using Protégé. The MAB-Ontology was evaluated through competency questions, use-case scenario, face validity and was found to satisfy the requirement specification. This study provides a unified method for developing a computerized-based adherence model that can be applied among various disease groups and different drug categories

    Suitability of Fast Healthcare Interoperability Resources (FHIR) for Wellness Data

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    Wellness data generated by patients using smart phones and portable devices can be a key part of Personal Health Record (PHR) data and offers healthcare service providers (healthcare providers) patient health information on a daily basis. Prior research has identified the potential for improved communication between healthcare provider and patient. However the practice of sharing patient generated wellness data has not been widely adopted by the healthcare sector; one of the reasons being the lack of interoperability preventing successful integration of such device generated data into the PHR and Electronic Health Record (EHR) systems. To address the interoperability issue it is important to make sure that wellness data can be supported in healthcare information exchange standards. Fast Healthcare Interoperability Resources (FHIR) is used in the current research study to identify the technical feasibility for patient generated wellness data. FHIR is expected to be the future healthcare information exchange standard in the healthcare industry. \ A conceptual data model of wellness data was developed for evaluation using FHIR standard. The conceptual data model contained blood glucose readings, blood pressure readings and Body Mass Index (BMI) data and could be extended to accept other types of wellness data. The wellness data model was packaged in an official FHIR resource called Observation. The research study proved the flexibility of adding new data elements related to wellness in Observation. It met the requirements in FHIR to include such data elements useful in self-management of chronic diseases. It also had the potential in sharing it with the healthcare provider system.

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

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    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

    Digital healthcare empowering Europeans:proceedings of MIE2015

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    D-WISE: Diabetes Web-Centric Information and Support Environment: Conceptual Specification and Proposed Evaluation

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    AbstractObjectiveTo develop and evaluate Diabetes Web-Centric Information and Support Environment (D-WISE) that offers 1) a computerized decision-support system to assist physicians to A) use the Canadian Diabetes Association clinical practice guidelines (CDA CPGs) to recommend evidence-informed interventions; B) offer a computerized readiness assessment strategy to help physicians administer behaviour-change strategies to help patients adhere to disease self-management programs; and 2) a patient-specific diabetes self-management application, accessible through smart mobile devices, that offers behaviour-change interventions to engage patients in self-management.MethodsThe above-mentioned objectives were pursued through a knowledge management approach that involved 1) Translation of paper-based CDA CPGs and behaviour-change models as computerized decision-support tools that will assist physicians to offer evidence-informed and personalized diabetes management and behaviour-change strategies; 2) Engagement of patients in their diabetes care by generating a diabetes self-management program that takes into account their preferences, challenges and needs; 3) Empowering patients to self-manage their condition by providing them with personalized educational and motivational messages through a mobile self-management application. The theoretical foundation of our research is grounded in behaviour-change models and healthcare knowledge management.We used 1) knowledge modelling to computerize the paper-based CDA CPGs and behaviour-change models, in particular, the behaviour-change strategy elements of A) readiness-to-change assessments; B) motivation-enhancement interventions categorized along the lines of patients' being ready, ambivalent or not ready; and C) self-efficacy enhancement. The CDA CPGs and the behaviour-change models are modelled and computerized in terms of A) a diabetes management ontology that serves as the knowledge resource for all the services offered by D-WISE; B) decision support services that use logic-based reasoning algorithms to utilize the knowledge encoded within the diabetes management ontology to assist physicians by recommending patient-specific diabetes-management interventions and behaviour-change strategies; C) a mobile diabetes self-management application to engage and educate diabetes patients to self-manage their condition in a home-based setting while working in concert with their family physicians.ResultsWe have been successful in creating and conducting a usability assessment of the physician decision support tool. These results will be published once the patient self- management application has been evaluated.ConclusionsD-WISE will be evaluated through pilot studies measuring 1) the usability of the e-Health interventions; and 2) the impact of the interventions on patients' behaviour changes and diabetes control

    Towards a mobile system for hypertensive outpatients' treatment adherence improvement

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    Covering more than a third of the population, arterial hypertension is a debilitating disease resulting in the adverse effect on the physical and emotional state of the patient and, hence, exerting the negative influence on the patient health- related quality of life. Treatment of hypertension involves the use of specific drug therapy along with a modification of a lifestyle and a diet over a long-term period. This, in turn, leads to the low adherence to the treatment among the ambulatory patients and, as a consequence, increases the chances of the hypertension-related complications, including the risk of sudden cardiac death. To address the problem of low adherence, we have previously proposed the mobile personal monitoring and assisting system constructed on the principles of smart spaces. The system relies on joint processing of both objective and subjective health measures accumulated in semantic ontology-driven storage enabling the construction of the personalized assisting services. In this paper, we extend the approach putting into consideration behaviour activities and interventions. Moreover, we propose the adherence assessment method based on the variety of user engagement measures, which also can be divided into subjective questionnaire-based measures, and objective metrics based on behaviour analysis and mobile app analytics

    Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert

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    A major challenge related to caring for patients with chronic conditions is the early detection of exacerbations of the disease. Medical personnel should be contacted immediately in order to intervene in time before an acute state is reached, ensuring patient safety. This paper proposes an approach to an ambient intelligence (AmI) framework supporting real-time remote monitoring of patients diagnosed with congestive heart failure (CHF). Its novelty is the integration of: (i) personalized monitoring of the patients health status and risk stage; (ii) intelligent alerting of the dedicated physician through the construction of medical workflows on-the-fly; and (iii) dynamic adaptation of the vital signs' monitoring environment on any available device or smart phone located in close proximity to the physician depending on new medical measurements, additional disease specifications or the failure of the infrastructure. The intelligence lies in the adoption of semantics providing for a personalized and automated emergency alerting that smoothly interacts with the physician, regardless of his location, ensuring timely intervention during an emergency. It is evaluated on a medical emergency scenario, where in the case of exceeded patient thresholds, medical personnel are localized and contacted, presenting ad hoc information on the patient's condition on the most suited device within the physician's reach

    Performance Evaluation of Smart Decision Support Systems on Healthcare

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    Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

    Patient generated health data and electronic health record integration, governance and socio-technical issues: A narrative review

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    Patients’ health records have the potential to include patient generated health data (PGHD), which can aid in the provision of personalized care. Access to these data can allow healthcare professionals to receive additional information that will assist in decision-making and the provision of additional support. Given the diverse sources of PGHD, this review aims to provide evidence on PGHD integration with electronic health records (EHR), models and standards for PGHD exchange with EHR, and PGHD-EHR policy design and development. The review also addresses governance and socio-technical considerations in PGHD management. Databases used for the review include PubMed, Scopus, ScienceDirect, IEEE Xplore, SpringerLink and ACM Digital Library. The review reveals the significance, but current deficiency, of provenance, trust and contextual information as part of PGHD integration with EHR. Also, we find that there is limited work on data quality, and on new data sources and associated data elements, within the design of existing standards developed for PGHD integration. New data sources from emerging technologies like mixed reality, virtual reality, interactive voice response system, and social media are rarely considered. The review recommends the need for well-developed designs and policies for PGHD-EHR integration that promote data quality, patient autonomy, privacy, and enhanced trust

    Serum Metabolomics and Proteomics to Study the Antihypertensive Effect of Protein Extracts from Tenebrio molitor

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    Hypertension is the leading risk factor for premature death worldwide and significantly contributes to the development of all major cardiovascular disease events. The management of high blood pressure includes lifestyle changes and treatment with antihypertensive drugs. Recently, it was demonstrated that a diet supplemented with Tenebrio molitor (TM) extracts is useful in the management of numerous pathologies, including hypertension. This study is aimed at unveiling the underlying mechanism and the molecular targets of intervention of TM dietary supplementation in hypertension treatment by means of proteomics and metabolomics techniques based on liquid chromatography coupled with high-resolution mass spectrometry. We demonstrate that serum proteome and metabolome of spontaneously hypertensive rats are severely altered with respect to their normotensive counterparts. Additionally, our results reveal that a diet enriched with TM extracts restores the expression of 15 metabolites and 17 proteins mainly involved in biological pathways associated with blood pressure maintenance, such as the renin-angiotensin and kallikrein-kinin systems, serin protease inhibitors, reactive oxygen scavenging, and lipid peroxidation. This study provides novel insights into the molecular pathways that may underlie the beneficial effects of TM, thus corroborating that TM could be proposed as a helpful functional food supplement in the treatment of hypertension
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