Preface XIIL clinical practice, a disease is studied from a specific perspective (e.g, genetic, pathologic, radiologic, clinical). But disease is a phenomenon of nature, and is thus typically multifaceted in its presentation. The goal is to aggregate the observations for a single patient to characterize the state and behavior of the patient's disease, both in terms of its natural course and as the result of (therapeutic)interventions The chapter divides the organization of such information along spatial (e.g physical and anatomical relations, such as between objects in space), temporal (e.g, sequences of clinical events, episodes of care), and clinically-oriented models (i. e, those models specific to representing a healthcare abstraction a discussion of the motivation behind what drives the design of a medical data model is given, leading to the description of a phenomenon-centric data model to support healthcare research Finally, in Part IV, Toward Medical Decision Making, we reflect on issues pertain- ng to reasoning with clinical observations derived from imaging and other data sources in order to reach a conclusion about patient care and the value of our decision A variety of formalisms are used to represent disease models, of these, probabilistic graphical models have become increasingly popular given their ability to reason in light of missing data, and their relatively intuitive representation. Chapter 8 Disease Models, Part 1: Graphical Models) commences with a review of key concepts in probability theory as the basis for understanding these graphical models and their different formulations. In particular, the first half of the chapter handles Bayesian belief networks(BBNS), appraising past and current efforts to apply these models to the medical environment. The latter half of this chapter addresses he burgeoning exploration of causal models, and the implications for analysis and positing questions to such networks. Throughout, a discussion of the practical considerations in the building of these models and the assumptions that must be Following the discussion of the creation of the models, in Chapter 9(Disease Models, Part 1: Querying Applications), we address the algorithms and tools that enable us to query BBNs. Two broad classes of queries are considered: belief updating, and abductive reasoning. The former entails the re-computation of pos- terior probabilities in a network given some specific evidence; the latter involves calculating the optimal configuration of the bBn in order to maximize some specified criteria. Brief descriptions of exact and approximate inference methods are provided. Special types of belief networks(naive Bayes classifiers, influence diagrams, probabilistic relational models)are covered, illustrating their potential usage in medicine. Importantly, issues related to the evaluation of belief networks are discussed in this chapter, looking to standard technical accuracy metrics, but also ideas in parametric sensitivity analysis. Lastly, the chapter concludes with some example applications of BBNs in medicine, including to support case-based retrieval and image processing tasks Chapter 10(Evaluation) concludes by considering how to assess informatics endeavors. A primer on biostatistics and study design starts this chapter, including a review of basic concepts(e.g, confidence intervals, significance and hypothesis testing)and the statistical tests that are used to evaluate hypotheses under differ- ent circumstances and assumptions. a discussion of error and performance assessment is then introduced, including sensitivity/specificity and receiver opera- tive characteristic analysis Study design encompasses a description of the differ ent types of experiments that can be formed to test a hypothesis, and goes over thePreface xiii clinical practice, a disease is studied from a specific perspective (e.g., genetic, pathologic, radiologic, clinical). But disease is a phenomenon of nature, and is thus typically multifaceted in its presentation. The goal is to aggregate the observations for a single patient to characterize the state and behavior of the patient’s disease, both in terms of its natural course and as the result of (therapeutic) interventions. The chapter divides the organization of such information along spatial (e.g., physical and anatomical relations, such as between objects in space), temporal (e.g., sequences of clinical events, episodes of care), and clinically-oriented models (i.e., those models specific to representing a healthcare abstraction). A discussion of the motivation behind what drives the design of a medical data model is given, leading to the description of a phenomenon-centric data model to support healthcare research. Finally, in Part IV, Toward Medical Decision Making, we reflect on issues pertain￾ing to reasoning with clinical observations derived from imaging and other data sources in order to reach a conclusion about patient care and the value of our decision: ƒ A variety of formalisms are used to represent disease models; of these, probabilistic graphical models have become increasingly popular given their ability to reason in light of missing data, and their relatively intuitive representation. Chapter 8 (Disease Models, Part I: Graphical Models) commences with a review of key concepts in probability theory as the basis for understanding these graphical models and their different formulations. In particular, the first half of the chapter handles Bayesian belief networks (BBNs), appraising past and current efforts to apply these models to the medical environment. The latter half of this chapter addresses the burgeoning exploration of causal models, and the implications for analysis and positing questions to such networks. Throughout, a discussion of the practical considerations in the building of these models and the assumptions that must be made, are given. ƒ Following the discussion of the creation of the models, in Chapter 9 (Disease Models, Part II: Querying & Applications), we address the algorithms and tools that enable us to query BBNs. Two broad classes of queries are considered: belief updating, and abductive reasoning. The former entails the re-computation of pos￾terior probabilities in a network given some specific evidence; the latter involves calculating the optimal configuration of the BBN in order to maximize some specified criteria. Brief descriptions of exact and approximate inference methods are provided. Special types of belief networks (naïve Bayes classifiers, influence diagrams, probabilistic relational models) are covered, illustrating their potential usage in medicine. Importantly, issues related to the evaluation of belief networks are discussed in this chapter, looking to standard technical accuracy metrics, but also ideas in parametric sensitivity analysis. Lastly, the chapter concludes with some example applications of BBNs in medicine, including to support case-based retrieval and image processing tasks. ƒ Chapter 10 (Evaluation) concludes by considering how to assess informatics endeavors. A primer on biostatistics and study design starts this chapter, including a review of basic concepts (e.g., confidence intervals, significance and hypothesis testing) and the statistical tests that are used to evaluate hypotheses under differ￾ent circumstances and assumptions. A discussion of error and performance assessment is then introduced, including sensitivity/specificity and receiver opera￾tive characteristic analysis. Study design encompasses a description of the differ￾ent types of experiments that can be formed to test a hypothesis, and goes over the