367 research outputs found

    Evolution and challenges in the design of computational systems for triage assistance

    Get PDF
    AbstractCompared with expert systems for specific disease diagnosis, knowledge-based systems to assist decision making in triage usually try to cover a much wider domain but can use a smaller set of variables due to time restrictions, many of them subjective so that accurate models are difficult to build. In this paper, we first study criteria that most affect the performance of systems for triage assistance. Such criteria include whether principled approaches from machine learning can be used to increase accuracy and robustness and to represent uncertainty, whether data and model integration can be performed or whether temporal evolution can be modeled to implement retriage or represent medication responses. Following the most important criteria, we explore current systems and identify some missing features that, if added, may yield to more accurate triage systems

    Non-communicable Diseases, Big Data and Artificial Intelligence

    Get PDF
    This reprint includes 15 articles in the field of non-communicable Diseases, big data, and artificial intelligence, overviewing the most recent advances in the field of AI and their application potential in 3P medicine

    Social networks : the future for health care delivery

    Get PDF
    With the rapid growth of online social networking for health, health care systems are experiencing an inescapable increase in complexity. This is not necessarily a drawback; self-organising, adaptive networks could become central to future health care delivery. This paper considers whether social networks composed of patients and their social circles can compete with, or complement, professional networks in assembling health-related information of value for improving health and health care. Using the framework of analysis of a two-sided network – patients and providers – with multiple platforms for interaction, we argue that the structure and dynamics of such a network has implications for future health care. Patients are using social networking to access and contribute health information. Among those living with chronic illness and disability and engaging with social networks, there is considerable expertise in assessing, combining and exploiting information. Social networking is providing a new landscape for patients to assemble health information, relatively free from the constraints of traditional health care. However, health information from social networks currently complements traditional sources rather than substituting for them. Networking among health care provider organisations is enabling greater exploitation of health information for health care planning. The platforms of interaction are also changing. Patient-doctor encounters are now more permeable to influence from social networks and professional networks. Diffuse and temporary platforms of interaction enable discourse between patients and professionals, and include platforms controlled by patients. We argue that social networking has the potential to change patterns of health inequalities and access to health care, alter the stability of health care provision and lead to a reformulation of the role of health professionals. Further research is needed to understand how network structure combined with its dynamics will affect the flow of information and potentially the allocation of health care resources

    Intelligent Systems for Sustainable Person-Centered Healthcare

    Get PDF
    This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregivers’ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life

    Aplicación de redes bayesianas en el modelado de un sistema experto de triaje en servicios de urgencias médicas

    Get PDF
    Este articulo describe el trabajo actual que estamos realizando para la aplicación de redes bayesianas en el modelado de sistemas expertos de triaje (clasificación) en los servicios de urgencias médicas. Las redes son construidas teniendo en cuenta tanto los datos provenientes de experiencias de triaje como la opinión de médicos expertos en urgencias. El sistema será utilizado con una doble finalidad: a nivel teórico para entender cómo la información requerida en el triaje puede ser modelada mediante redes bayesianas y a nivel práctico para entrenamiento y uso por el personal de triaje.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

    Aplicación de redes bayesianas en el modelado de un sistema experto de triaje en servicios de urgencias médicas

    Get PDF
    Este articulo describe el trabajo actual que estamos realizando para la aplicación de redes bayesianas en el modelado de sistemas expertos de triaje (clasificación) en los servicios de urgencias médicas. Las redes son construidas teniendo en cuenta tanto los datos provenientes de experiencias de triaje como la opinión de médicos expertos en urgencias. El sistema será utilizado con una doble finalidad: a nivel teórico para entender cómo la información requerida en el triaje puede ser modelada mediante redes bayesianas y a nivel práctico para entrenamiento y uso por el personal de triaje.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

    Big data analytics for preventive medicine

    Get PDF
    © 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

    Machine learning in predicting immediate and long-term outcomes of myocardial revascularization: a systematic review

    Get PDF
    Machine learning (ML) is among the main tools of artificial intelligence and are increasingly used in population and clinical cardiology to stratify cardiovascular risk. The systematic review presents an analysis of literature on using various ML methods (artificial neural networks, random forest, stochastic gradient boosting, support vector machines, etc.) to develop predictive models determining the immediate and long-term risk of adverse events after coronary artery bypass grafting and percutaneous coronary intervention. Most of the research on this issue is focused on creation of novel forecast models with a higher predictive value. It is emphasized that the improvement of modeling technologies and the development of clinical decision support systems is one of the most promising areas of digitalizing healthcare that are in demand in everyday professional activities

    Intelligent Systems for Sustainable Person-Centered Healthcare

    Get PDF
    This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregivers’ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life

    Opportunities and Challenges in Healthcare Information Systems Research: Caring for Patients with Chronic Conditions

    Get PDF
    To prepare for the 2030 “baby-boomer challenge”, some governments have begun to implement healthcare reforms over the past two decades. These reforms have led healthcare information systems (HIS) to evolve into a major research area in our discipline. This research area has an increasing individual, organizational, and economic impact. Due to the 2030 “baby-boomer challenge”, the number of elderly individuals continues to increase, and they may have chronic illnesses, such as eye problems and Alzheimer’s disease. Given the practical need for HIS that support chronic care, we decided to conduct a literature synthesis and identify opportunities for HIS research. Specifically, we present the chronic care model and analyze how IS researchers have discussed HIS to address the needs of patients with chronic illness. Further, we identify research gaps and discuss the research topics on HIS that future work can extend and customize to support these patients. Our results stimulate and guide future research in the HIS area. This paper has the potential to strengthen the body of knowledge on HIS
    corecore