An authorization policy management framework for dynamic medical data sharing

In this paper, we propose a novel feature reduction approach to group words hierarchically into clusters which can then be used as new features for document classification. Initially, each word constitutes a cluster. We calculate the mutual confidence between any two different words. The pair of clusters containing the two words with the highest mutual confidence are combined into a new cluster. This process of merging is iterated until all the mutual confidences between the un-processed pair of words are smaller than a predefined threshold or only one cluster exists. In this way, a hierarchy of word clusters is obtained. The user can decide the clusters, from a certain level, to be used as new features for document classification. Experimental results have shown that our method can perform better than other methods.<br /

Securely sharing dynamic medical information in e-health

This thesis has introduced an infrastructure to share dynamic medical data between mixed health care providers in a secure way, which could benefit the health care system as a whole. The study results of the universally data sharing into a varied patient information system prototypes

A Study on the Conceptual Frame Work of Dataware Housing in Health Sector in Pakistan A Case Study of a Hospital System and Disease (Hepatitis C)

The decisions of people about medical treatments have a great impact on their lives. Health related sectors and patients often make decisions without complete understanding of the medical circumstances. The main cause is that health related data are available in fragmented, disparate and heterogeneous data silos. Without a centralized data warehouse structure to integrate these data silos, it is highly unlikely and impractical for the users (of concerned departments) to get all the information required on time to make a correct decision.  In this research study, a case study has been discussed  hospital system  and a disease (Hepatitis C only ) through literature review, it was found that in government hospital the data recorded  analyzed in old fashioned, manually where as in private hospitals  there is computerized Database to record the patients data, but failure is that there is no coordination between government and private hospitals to share the patients history and also no concept of Database overall in government hospitals in  Pakistan. After all , in this research study, a conceptual frame work has been discussed and proposed model for implementation of Dataware house in   health sector to centralized the data for analysis and decision making .Outcomes of this research are: one is ; documenting the comparisons of data warehousing architectures, logical and conceptual data warehousing models, other is proposing a data-warehousing model ( taking one disease Hepatitis C )  as model for implementation which is suitable for Government Health Sector as well as  other concerned departments  in Pakistan.ext fonts are prescribed; please do not alter them

Internet of Things (IoT) for Automated and Smart Applications

Internet of Things (IoT) is a recent technology paradigm that creates a global network of machines and devices that are capable of communicating with each other. Security cameras, sensors, vehicles, buildings, and software are examples of devices that can exchange data between each other. IoT is recognized as one of the most important areas of future technologies and is gaining vast recognition in a wide range of applications and fields related to smart homes and cities, military, education, hospitals, homeland security systems, transportation and autonomous connected cars, agriculture, intelligent shopping systems, and other modern technologies. This book explores the most important IoT automated and smart applications to help the reader understand the principle of using IoT in such applications

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence

Investigating the impact of health analytics on the cost and quality of care for patients with heart failure

The healthcare industry is under tremendous pressure to improve the quality of care and provide more patient centric care, while reducing costs. The potential use of data analytics to address these health system issues has raised significant interest in both research and practice. Health Analytics is central to informing and realizing the systematic quality improvements and cost reductions required by healthcare reform. Fundamentally, the contribution of IS and analytics research in healthcare is to identify and study the impact of interventions that can make a significant difference to the quality and cost of care. This dissertation is concentrated on patients with heart failure (HF). HF is the number one killer in the world, and is the largest contributor to healthcare costs in the United States. Moreover, HF is one of the six conditions used by the Centers for Medicare and Medicaid Services (CMS) to exercise fiduciary control over health systems by monitoring both the quality and cost of care. Specifically, my larger research question is “How can we identify and inform impactful transition of care interventions that manage costs and improve resource allocation efficiencies while providing improved quality of care for heart failure patients?” We adopted a mixed-method approach to study the impact of transitional care in a healthcare system for patients with heart failure. This dissertation includes three essays. In the first essay, I use qualitative methods to study the nature, sources and impacts of information coordination problems as HF patients’ transition through the patient flow in a health system. I propose a set of interventions based on my analysis of information and control errors along the continuum of care to inform the design of appropriate interventions that improve the cost and quality of care. In the second essay, I empirically evaluate the impact of these interventions on cost and quality of care measures such as all cause readmissions, heart failure readmissions, ER visits, length of stay, and cost of care. Analysis suggests that multicomponent complex transitional interventions have significant impact on reducing 30-day readmission and ER visits. The third essay is dedicated to understanding the impact of heart failure patient’s self-care behaviors. I developed and validated an assessment tool for patients with heart failure to monitor and score their condition accurately. Together, these essays investigate impactful transition of care interventions that can help healthcare organizations improve quality of care and manage costs from the clinical, administrative and patient perspectives

pHealth 2021. Proc. of the 18th Internat. Conf. on Wearable Micro and Nano Technologies for Personalised Health, 8-10 November 2021, Genoa, Italy

Smart mobile systems – microsystems, smart textiles, smart implants, sensor-controlled medical devices – together with related body, local and wide-area networks up to cloud services, have become important enablers for telemedicine and the next generation of healthcare services. The multilateral benefits of pHealth technologies offer enormous potential for all stakeholder communities, not only in terms of improvements in medical quality and industrial competitiveness, but also for the management of healthcare costs and, last but not least, the improvement of patient experience. This book presents the proceedings of pHealth 2021, the 18th in a series of conferences on wearable micro and nano technologies for personalized health with personal health management systems, hosted by the University of Genoa, Italy, and held as an online event from 8 – 10 November 2021. The conference focused on digital health ecosystems in the transformation of healthcare towards personalized, participative, preventive, predictive precision medicine (5P medicine). The book contains 46 peer-reviewed papers (1 keynote, 5 invited papers, 33 full papers, and 7 poster papers). Subjects covered include the deployment of mobile technologies, micro-nano-bio smart systems, bio-data management and analytics, autonomous and intelligent systems, the Health Internet of Things (HIoT), as well as potential risks for security and privacy, and the motivation and empowerment of patients in care processes. Providing an overview of current advances in personalized health and health management, the book will be of interest to all those working in the field of healthcare today