Machine Learning for Cardiovascular Disease Risk Assessment: A Systematic Review

Accurate diagnosis and early detection of heart disease can help save lives because it is the primary cause of mortality. If a forecast is inaccurate, patients could potentially suffer significant harm. Today, it is challenging to predict and identify heart disease. 24 hour monitoring is not practical due to the extensive equipment and time required. Heart disease treatments can be both expensive and challenging. In order to obtain the data from databases and use this information to successfully forecast cardiac illness, a variety of data mining techniques and machine learning algorithms are now accessible. We have used every technique to put the heart disease prognosis into practise. The algorithms used in SVM, NAIVE BAYER, REGRESSION, KNN, ADABOOST, DECISION TREE, and XG-BOOST And Voting Ensemble Method

Automatic Generation of Personalized Recommendations in eCoaching

Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

Health State Estimation

Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.Comment: Ph.D. Dissertation @ University of California, Irvin

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Mood-tracking application as persuasive technology for reduction of occupational stress

Occupational stress is a major concern in the job performance of employees. The use of persuasive technologies is one way to prevent or reduce this stress. The present study developed a mood tracking application and tested its effect on stress reduction of employees. Twenty-six employees of a petrochemical company were divided into equally-sized groups of married and single participants. Employees' accommodations are different based on their marital status. Other variables effecting stress were same for both groups. An OSI-R questionnaire was used to collect data before and after four weeks of application use. In present pilot study, data analysis shows that this kind of persuasive technology can have a significant positive effect on single employees and help them to decrease and manage their stress; however, no meaningful results were recorded for the married group because they did not use the application as much as participants in the single group

The Design of Interactive Visualizations and Analytics for Public Health Data

Public health data plays a critical role in ensuring the health of the populace. Professionals use data as they engage in efforts to improve and protect the health of communities. For the public, data influences their ability to make health-related decisions. Health literacy, which is the ability of an individual to access, understand, and apply health data, is a key determinant of health. At present, people seeking to use public health data are confronted with a myriad of challenges some of which relate to the nature and structure of the data. Interactive visualizations are a category of computational tools that can support individuals as they seek to use public health data. With interactive visualizations, individuals can access underlying data, change how data is represented, manipulate various visual elements, and in certain tools control and perform analytic tasks. That being said, currently, in public health, simple visualizations, which fail to effectively support the exploration of large sets of data, are predominantly used. The goal of this dissertation is to demonstrate the benefit of sophisticated interactive visualizations and analytics. As improperly designed visualizations can negatively impact users’ discourse with data, there is a need for frameworks to help designers think systematically about design issues. Furthermore, there is a need to demonstrate how such frameworks can be utilized. This dissertation includes a process by which designers can create health visualizations. Using this process, five novel visualizations were designed to facilitate making sense of public health data. Three studies were conducted with the visualizations. The first study explores how computational models can be used to make sense of the discourse of health on a social media platform. The second study investigates the use of instructional materials to improve visualization literacy. Visualization literacy is important because even when visualizations are designed properly, there still exists a gap between how a tool works and users’ perceptions of how the tool should work. The last study examines the efficacy of visualizations to improve health literacy. Overall then, this dissertation provides designers with a deeper understanding of how to systematically design health visualizations

Data Science in Healthcare

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management