Computational intelligence contributions to readmisision risk prediction in Healthcare systems

136 p.The Thesis tackles the problem of readmission risk prediction in healthcare systems from a machine learning and computational intelligence point of view. Readmission has been recognized as an indicator of healthcare quality with primary economic importance. We examine two specific instances of the problem, the emergency department (ED) admission and heart failure (HF) patient care using anonymized datasets from three institutions to carry real-life computational experiments validating the proposed approaches. The main difficulties posed by this kind of datasets is their high class imbalance ratio, and the lack of informative value of the recorded variables. This thesis reports the results of innovative class balancing approaches and new classification architectures

Computational intelligence contributions to readmisision risk prediction in Healthcare systems

136 p.The Thesis tackles the problem of readmission risk prediction in healthcare systems from a machine learning and computational intelligence point of view. Readmission has been recognized as an indicator of healthcare quality with primary economic importance. We examine two specific instances of the problem, the emergency department (ED) admission and heart failure (HF) patient care using anonymized datasets from three institutions to carry real-life computational experiments validating the proposed approaches. The main difficulties posed by this kind of datasets is their high class imbalance ratio, and the lack of informative value of the recorded variables. This thesis reports the results of innovative class balancing approaches and new classification architectures

The Meaningful Use of Cloud Computing in Healthcare

This dissertation focuses on the meaning of cloud computing for healthcare and its meaningful use in the healthcare industry. If used in a meaningful way, cloud computing is argued to be able to provide major benefits to the healthcare industry. Surprisingly, the benefits promised by using cloud computing often do not hold in practice, and the deployment of cloud computing services in healthcare organizations could lead to countereffects for healthcare. Although existing research studies cover a wide range of domains in healthcare, they often do not explain the way in which cloud computing could support healthcare in a systematic manner. In reply to that insufficiency in the research, this dissertation aims to investigate the phenomenon of cloud computing in healthcare organizations and to answer the following overarching research question: How can cloud computing support healthcare organizations in a meaningful way (i.e., meaningful use)? This dissertation conducted four research studies by employing established explorative research methods. The dissertation begins with a study (study 1) that investigates the basic properties of cloud computing services and their specific meanings for the healthcare industry, and suggests concrete directions for studies related to the meaningful use of cloud computing in healthcare. Study 2 focuses on the identification of industry-specific factors for the adoption of cloud computing services in healthcare, and studies 3 and 4 on an investigation of the way in which cloud computing supports collaborative activities in healthcare, respectively. Both focuses belong to research directions suggested by study 1. By addressing the overarching research question, this dissertation could deepen our understanding of the use of information technology (IT) artefacts that advances information systems theories, not only regarding cloud computing itself but also in terms of more general health IT levels