Foundations of Biomedical Knowledge Representation

Medicine and health care are currently faced with a significant rise in their complexity. This is partly due to the progress made during the past three decades in the fundamental biological understanding of the causes of health and disease at the molecular, (sub)cellular, and organ level. Since the end of the 1970s, when knowledge representation and reasoning in the biomedical field became a separate area of research, huge progress has been made in the development of methods and tools that are finally able to impact on the way medicine is being practiced.

Even though there are huge differences in the techniques and methods used by biomedical researchers, there is now an increasing tendency to share research results in terms of formal knowledge representation methods, such as ontologies, statistical models, network models, and mathematical models. As there is an urgent need for health-care professionals to make better decisions, computer-based support using this knowledge is now becoming increasingly important. It may also be the only way to integrate research results from the different parts of the spectrum of biomedical and clinical research.

The aim of this book is to shed light on developments in knowledge representation at different levels of biomedical application, ranging from human biology to clinical guidelines, and using different techniques, from probability theory and differential equations to logic. The book starts with two introductory chapters followed by 18 contributions organized in the following topical sections: diagnosis of disease; monitoring of health and disease and conformance; assessment of health and personalization; prediction and prognosis of health and disease; treatment of disease; and recommendations.

Inhalt

How to Read the Book "Foundations of Biomedical Knowledge Representation".- An Introduction to Knowledge Representation and Reasoning in Healthcare.- Representing Knowledge for Clinical Diagnostic Reasoning.- Automated Diagnosis of Breast Cancer on Medical.- Monitoring in the Healthcare Setting.- Conformance Verification of Clinical Guidelines in Presence of Computerized and Human-Enhanced.- Modelling and Monitoring the Individual Patient in Real Time.- Personalised Medicine: Taking a New Look at the Patient.- Graphical Modelling in Genetics and Systems Biology.- Chain Graphs and Gene Networks.- Prediction and Prognosis of Health and Disease.- Trajectories Through the Disease Process: Cross Sectional and Longitudinal Studies.- Dynamic Bayesian Network for Cervical Cancer Screening.- Modeling Dynamic Processes with Memory by Higher Order Temporal Models.- Treatment of Disease: The Role of Knowledge Representation for Treatment Selection.- Predicting Adverse Drug Events from Electronic Medical Records.- User Modelling for Patient Tailored Virtual Rehabilitation.- Supporting Physicians and Patients through Recommendation: Guidelines and Beyond.- A Hybrid Approach to the Verification of Computer Interpretable Guidelines.- Aggregation of Clinical Evidence Using Argumentation.