Model-guided therapy for hepatocellular carcinoma: A role for information technology in predictive, preventive and personalized medicine

Predictive, preventive and personalized medicine (PPPM) may have the potential to eventually improve the nature of health care delivery. However, the tools required for a practical and comprehensive form of PPPM that is capable of handling the vast amounts of medical information that is currently available are currently lacking. This article reviews a rationale and method for combining and integrating diagnostic and therapeutic management with information technology (IT), in a manner that supports patients through their continuum of care. It is imperative that any program devised to explore and develop personalized health care delivery must be firmly rooted in clinically confirmed and accepted principles and technologies. Therefore, a use case, relating to hepatocellular carcinoma (HCC), was developed. The approach to the management of medical information we have taken is based on model theory and seeks to implement a form of model-guided therapy (MGT) that can be used as a decision support system in the treatment of patients with HCC. The IT structures to be utilized in MGT include a therapy imaging and model management system (TIMMS) and a digital patient model (DPM). The system that we propose will utilize patient modeling techniques to generate valid DPMs (which factor in age, physiologic condition, disease and co-morbidities, genetics, biomarkers and responses to previous treatments). We may, then, be able to develop a statistically valid methodology, on an individual basis, to predict certain diseases or conditions, to predict certain treatment outcomes, to prevent certain diseases or complications and to develop treatment regimens that are personalized for that particular patient. An IT system for predictive, preventive and personalized medicine (ITS-PM) for HCC is presented to provide a comprehensive system to provide unified access to general medical and patient-specific information for medical researchers and health care providers from different disciplines including hepatologists, gastroenterologists, medical and surgical oncologists, liver transplant teams, interventional radiologists and radiation oncologists. The article concludes with a review providing an outlook and recommendations for the application of MGT to enhance the medical management of HCC through PPPM

Electronic Portfolios Enhanced with Learning Analytics at the Workplace

This chapter shows the development of an ePortfolio environment enhanced with learning analytics, to be used at the workplace in medical, veterinary, and teacher education. Evaluation took place by means of a quasi-experimental design regarding the impact of this environment on trainees’ motivation, their assessment experience, and their use. Data gathered in four institutes for medical, veterinary, and teacher education (n = 217) showed that trainees were highly motivated for their internships and positively evaluated the perceived feedback. The use of learning analytics features varied. In general visual feedback by means of a timeline of trainees’ progress was mostly used, while trainees barely used the features with written feedback. It is concluded that the promise of learning analytics connected to ePortfolios can only be fulfilled when developed and implemented through the eyes of the users

Traffic management for cloud federation

The chapter summarizes activities of COST IC1304 ACROSS European Project corresponding to traffic management for Cloud Federation (CF). In particular, we provide a survey of CF architectures and standardization activities. We present comprehensive multi-level model for traffic management in CF that consists of five levels: Level 5 - Strategies for building CF, Level 4 - Network for CF, Level 3 - Service specification and provision, Level 2 - Service composition and orchestration, and Level 1 - Task service in cloud resources. For each level we propose specific methods and algorithms. The effectiveness of these solutions were verified by simulation and analytical methods. Finally, we also describe specialized simulator for testing CF solution in IoT environment

Learning Probabilistic Description Logic Concepts Under Alternative Assumptions on Incompleteness

Real-world knowledge often involves various degrees of uncertainty. For such a reason, in the Semantic Web context, difficulties arise when modeling real-world domains using only purely logical formalisms. Alternative approaches almost always assume the availability of probabilistically-enriched knowledge, while this is hardly known in advance. In addition, purely deductive exact inference may be infeasible for Web-scale ontological knowledge bases, and does not exploit statistical regularities in data. Approximate deductive and inductive inferences were proposed to alleviate such problems. This article proposes casting the concept-membership prediction problem (predicting whether an individual in a Description Logic knowledge base is a member of a concept) as estimating a conditional probability distribution which models the posterior probability of the aforementioned individual’s concept-membership given the knowledge that can be entailed from the knowledge base regarding the individual. Specifically, we model such posterior probability distribution as a generative, discriminatively structured, Bayesian network, using the individual’s concept-membership w.r.t. a set of feature concepts standing for the available knowledge about such individual. Uncertainty Reasoning for the Semantic Web III Uncertainty Reasoning for the Semantic Web III Look Inside Other actions Reprints and Permissions Export citation About this Book Add to Papers Share Share this content on Facebook Share this content on Twitter Share this content on LinkedI