This book is an up-to-date self-contained compendium of the research carried out by the authors on model-based diagnosis of a class of discrete-event systems called active systems. After defining the diagnosis problem, the book copes with a variety of reasoning mechanisms that generate the diagnosis, possibly within a monitoring setting. The book is structured into twelve chapters, each of which has its own introduction and concludes with bibliographic notes and itemized summaries. Concepts and techniques are presented with the help of numerous examples, figures, and tables, and when appropriate these concepts are formalized into propositions and theorems, while detailed algorithms are expressed in pseudocode. This work is primarily intended for researchers, professionals, and graduate students in the fields of artificial intelligence and control theory.

Getting the right diagnosis is a key aspect of health care - it provides an explanation of a patient's health problem and informs subsequent health care decisions. The diagnostic process is a complex, collaborative activity that involves clinical reasoning and information gathering to determine a patient's health problem. According to Improving Diagnosis in Health Care, diagnostic errors-inaccurate or delayed diagnoses-persist throughout all settings of care and continue to harm an unacceptable number of patients. It is likely that most people will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences. Diagnostic errors may cause harm to patients by preventing or delaying appropriate treatment, providing unnecessary or harmful treatment, or resulting in psychological or financial repercussions. The committee concluded that improving the diagnostic process is not only possible, but also represents a moral, professional, and public health imperative. Improving Diagnosis in Health Care, a continuation of the landmark Institute of Medicine reports To Err Is Human (2000) and Crossing the Quality Chasm (2001), finds that diagnosis-and, in particular, the occurrence of diagnostic errorsâ€"has been largely unappreciated in efforts to improve the quality and safety of health care. Without a dedicated focus on improving diagnosis, diagnostic errors will likely worsen as the delivery of health care and the diagnostic process continue to increase in complexity. Just as the diagnostic process is a collaborative activity, improving diagnosis will require collaboration and a widespread commitment to change among health care professionals, health care organizations, patients and their families, researchers, and policy makers. The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.

Infectious diseases are the leading cause of death globally, particularly among children and young adults. The spread of new pathogens and the threat of antimicrobial resistance pose particular challenges in combating these diseases. Major Infectious Diseases identifies feasible, cost-effective packages of interventions and strategies across delivery platforms to prevent and treat HIV/AIDS, other sexually transmitted infections, tuberculosis, malaria, adult febrile illness, viral hepatitis, and neglected tropical diseases. The volume emphasizes the need to effectively address emerging antimicrobial resistance, strengthen health systems, and increase access to care. The attainable goals are to reduce incidence, develop innovative approaches, and optimize existing tools in resource-constrained settings.

This book is about model-based diagnosis of a class of discrete-event systems called active systems. Roughly, model-based diagnosis is the task of finding out the faulty components of a physical system based on the observed behavior and the system model. An active system is the abstraction of a physical artefact that is modeled as a network of com municating automata. For example, the protection apparatus of a power transmission network can be conveniently modeled as an active system, where breakers, protection devices, and lines are naturally described by finite state machines. The asynchronous occurrence of a short circuit on a line or a bus-bar causes the reaction of the protection devices, which aims to isolate the shorted line. This reaction can be faulty and several lines might be eventually isolated, rather than the shorted line only. The diagnostic problem to be solved is uncovering the faulty devices based the visible part of the reaction. Once the diagnosis task has been on accomplished, the produced results are exploited to fix the apparatus (and also to localize the short circuit, in this sample case). Interestingly, the research presented in this book was triggered a decade ago by a project 011 short circuit localization, conducted by ENEL, the Italian electricity board, along with other industrial and academic European partners.

v. 1. Research findings -- v. 2. Concepts and methodology -- v. 3. Implementation issues -- v. 4. Programs, tools and products.

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

This book constitutes the thoroughly refereed proceedings of the 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, held in Graz, Austria, in July 2019. The 41 full papers and 32 short papers presented were carefully reviewed and selected from 151 submissions. The IEA/AIE 2019 conference will continue the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas. These areas include engineering, science, industry, automation and robotics, business and finance, medicine and biomedicine, bioinformatics, cyberspace, and human-machine interactions. IEA/AIE 2019 will have a special focus on automated driving and autonomous systems and also contributions dealing with such systems or their verification and validation as well.