From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

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It is important that the medical profession play a significant role in critically evaluating the use of diagnostic procedures and therapies as they are introduced in the detection, management,

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ACC/AHA 2006 Guidelines for the Management of Patients With Valvular Heart Disease

This book presents the definitive exposition of 'prospect theory', a compelling alternative to the classical utility theory of choice. Building on the 1982 volume, Judgement Under Uncertainty, this book brings together seminal papers on prospect theory from economists, decision theorists, and psychologists, including the work of the late Amos Tversky, whose contributions are collected here for the first time. While remaining within a rational choice framework, prospect theory delivers more accurate, empirically verified predictions in key test cases, as well as helping to explain many complex, real-world puzzles. In this volume, it is brought to bear on phenomena as diverse as the principles of legal compensation, the equity premium puzzle in financial markets, and the number of hours that New York cab drivers choose to drive on rainy days. Theoretically elegant and empirically robust, this volume shows how prospect theory has matured into a new science of decision making.

Choices, Values, and Frames

Users of clinical practice guidelines and other recommendations need to know how much confidence they can place in the recommendations Systematic and explicit methods of making judgments can reduce errors and improve communication We have developed a system for grading the quality of evidence and the strength of recommendations that can be applied across a wide range of interventions and contexts In this article we present a summary of our approach from the perspective of a guideline user Judgments about the strength of a recommendation require consideration of the balance between benefits and harms, the quality of the evidence, translation of the evidence into specific circumstances, and the certainty of the baseline risk It is also important to consider costs (resource utilisation) before making a recommendation Inconsistencies among systems for grading the quality of evidence and the strength of recommendations reduce their potential to facilitate critical appraisal and improve communication of these judgments Our system for guiding these complex judgments balances the need for simplicity with the need for full and transparent consideration of all important issues

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Grading quality of evidence and strength of recommendations.

We discuss the cognitive and the psy- chophysical determinants of choice in risky and risk- less contexts. The psychophysics of value induce risk aversion in the domain of gains and risk seeking in the domain of losses. The psychophysics of chance induce overweighting of sure things and of improbable events, relative to events of moderate probability. De- cision problems can be described or framed in multiple ways that give rise to different preferences, contrary to the invariance criterion of rational choice. The pro- cess of mental accounting, in which people organize the outcomes of transactions, explains some anomalies of consumer behavior. In particular, the acceptability of an option can depend on whether a negative outcome is evaluated as a cost or as an uncompensated loss. The relation between decision values and experience values is discussed. Making decisions is like speaking prose—people do it all the time, knowingly or unknowingly. It is hardly surprising, then, that the topic of decision making is shared by many disciplines, from mathematics and statistics, through economics and political science, to sociology and psychology. The study of decisions ad- dresses both normative and descriptive questions. The normative analysis is concerned with the nature of rationality and the logic of decision making. The de- scriptive analysis, in contrast, is concerned with peo- ple's beliefs and preferences as they are, not as they should be. The tension between normative and de- scriptive considerations characterizes much of the study of judgment and choice. Analyses of decision making commonly distin- guish risky and riskless choices. The paradigmatic example of decision under risk is the acceptability of a gamble that yields monetary outcomes with specified probabilities. A typical riskless decision concerns the acceptability of a transaction in which a good or a service is exchanged for money or labor. In the first part of this article we present an analysis of the cog- nitive and psychophysical factors that determine the value of risky prospects. In the second part we extend this analysis to transactions and trades. Risky Choice Risky choices, such as whether or not to take an umbrella and whether or not to go to war, are made without advance knowledge of their consequences. Because the consequences of such actions depend on uncertain events such as the weather or the opponent's resolve, the choice of an act may be construed as the acceptance of a gamble that can yield various out- comes with different probabilities. It is therefore nat- ural that the study of decision making under risk has focused on choices between simple gambles with monetary outcomes and specified probabilities, in the hope that these simple problems will reveal basic at- titudes toward risk and value. We shall sketch an approach to risky choice that

http://psycnet.apa.org/doi/10.1037/0003-066X.39.4.341

AM, a 95-year-old semiretired lawyer, had been in excellent health all his life; he prided himself on having &dquo;the body of an 80-year-old.&dquo; A routine chest x ray performed in October 1981 was normal; another, ten months later, revealed a noncalcified density, 1.5 cm in diameter, in the right lung near the main bronchus and pulmonary artery. AM lost ten pounds during the summer of 1982, but had no other symptoms. The lesion was believed by the patient’s physicians to be almost certainly malignant, and was designated as a Stage I tumor in the TNM system of classification. At one well-known medical center, he was told that his age precluded further evaluation or specific antineoplastic therapy. Consultation at another hospital renowned for its expertise in oncology led to a recommendation that he undergo surgical resection of the lesion. The patient sought still another opinion, at the New England Medical Center. Because he had continuing responsibilities to two elderly siblings, he expressed a desire to live &dquo;a few more years&dquo; if he could do so with reasonable comfort.

What to do when the patient outlives the literature, or DEALE-ing with a full deck.

A 53-year-old woman was admitted to New England Medical Center with nephrotic syndrome. Nine years earlier, she had been found to have a blood pressure of 150/110 mmHg. At that time her 24 hour protein excretion was 900 mg, her blood urea nitrogen was 18 mg/dl, and her serum creatinine was 0.9 mg/dl. She was given propranolol and achieved good control of her blood pressure. One year before admission her protein excretion increased to 2.8 grams/24 hours and her serum creatinine was 1.1 mg/dl. At that time both the antinuclear antibody titer and the lupus erythematosus preparation

Idiopathic nephrotic syndrome in a 53-year-old woman. Is a kidney biopsy necessary?

we address the question of the optimal timing for surgery in an elderly woman with a recent myocardial infarction. Although the issue raised by competing risks has been addressed in prior analyses, the dynamics were slightly different here-both the risk of surgery and the risk of recurrent hemorrhage decline with time, but at different rates. In an elderly patient with a very limited life expectancy, the central issue is to avoid near-term catastrophes and not to prevent events that might occur many years later. The model chosen for this analysis was a recursive decision tree with a rather short time horizon. Sensitivity analyses addressed when elective surgery might be feasible and alternative rates of decline for these risks. This analysis was completed in a timely fashion and had a major impact on patient management.

Competing rates of risk in a patient with subarachnoid hemorrhage and myocardial infarction: it's now or never.

O “new” focus on patient-centered care emphasizes the need to understand how patients’ preferences and reasoning affect how they choose among alternative diagnostic and therapeutic options. Although recognition of variation in patients’ pathophysiology, preferences, and the setting of their care is far from new, until recently it had taken a back seat to standardization, guidelines, and the evidence of average outcomes. Indeed, evidence-based standardization and patient centeredness have been on collision courses. For the past 30 years this Journal and the Society for Medical Decision Making have examined and promulgated both normative approaches to choosing among alternatives and descriptive reports about the psychology and reality of clinical decision making. But few have examined the inputs and processes that govern patient choice. In this supplement to the Journal, 8 papers report on the DECISIONS Study, a retrospective survey of 3 important scenarios of choice: medication initiation, cancer screening, and elective surgery. This survey and these papers are an important step in discovering what patient-centered care will mean and what clinicians and researchers must do to transform the concept into a safe and effective reality. At the outset, consider 5 models of decision making, as outlined in Table 1. The classic model is no longer in vogue, but does crop up with some older clinicians or when a patient wishes to abdicate choice to a clinician he or she trusts, perhaps to avoid postdecisional regret after a bad outcome. Passive informed consent is fairly standard these days, although the nocebo effect may actually increase the chance of poor outcomes by creating negative expectations. In some circumstances, especially when the decision has been shown to be preference sensitive, active informed consent is used. One such setting is when counseling patients about genetic and reproductive choices. In active informed consent, patients may be provided with a decision aid to help improve their understanding of the problem. Shared decision making comes in 2 flavors— with and without a supporting decision model or decision aid—but in either case, the choice is a team project of the clinician–patient dyad. In the context of these models of how patients are or are not engaged in the decision process, let’s consider patient-centered care and the results and limitations of the DECISIONS Study. First, the survey upon which these papers are based was conducted more than 3 years ago. Although many things in medicine are inertia laden, much has changed, or is changing in American health care, and many of these changes have contributed to an increasing distrust of industry and likely an increasing distrust of physicians, who may also suffer from conflicts of interest: revelations about inadequate reporting of adverse events; break-neck marketing of new genetic tests with minimal validation; health care reform and the creation of fear and angst in patients for political purposes; the further explosion of Internet access at homes, airports, coffee shops and in iPads, Blackberries, and netbooks. Although the DECISIONS Study reports physicians and other health care providers are the most trusted source of medical information, these changes make one wonder if respect for physicians may wane, leading the “Wild West Web” and the ease of Google and similar search strategies to become the dominant informants. Likewise, the DECISIONS Study found that the availability of a primary care physician was an important determinant of the number of decisions a patient faces. (Note that I deliberately say primary care “physician,” because other clinicians may not have the breadth of knowledge required to support the growing number of diverse decisions peeking over the horizon.) But primary care physicians, including those who care for patients over age 40, From Tufts Medical Center, Boston, Massachusetts.

Medical Decision Making: How Patients Choose

Formal prescriptive models can help patients and clinicians better understand the risks and uncertainties they face and better formulate well-reasoned decisions. Using Bayes rule, the clinician can interpret pedigrees, historical data, physical findings and laboratory data, providing individualized probabilities of various diagnoses and outcomes of pregnancy. With the advent of screening programs for genetic disease, it becomes increasingly important to consider the prior probabilities of disease when interpreting an abnormal screening test result. Decision trees provide a convenient formalism for structuring diagnostic, therapeutic and reproductive decisions; such trees can also enhance communication between clinicians and patients. Utility theory provides a mechanism for patients to understand the choices they face and to communicate their attitudes about potential reproductive outcomes in a manner which encourages the integration of those attitudes into appropriate decisions. Using a decision tree, the relevant probabilities and the patients' utilities, physicians can estimate the relative worth of various medical and reproductive options by calculating the expected utility of each. By performing relevant sensitivity analyses, clinicians and patients can understand the impact of various soft data, including the patients' attitudes toward various health outcomes, on the decision making process. Formal clinical decision analytic models can provide deeper understanding and improved decision making in clinical genetics.

Stephen G. Pauker

Papers

What to do when the patient outlives the literature, or DEALE-ing with a full deck.

Idiopathic nephrotic syndrome in a 53-year-old woman. Is a kidney biopsy necessary?

Competing rates of risk in a patient with subarachnoid hemorrhage and myocardial infarction: it's now or never.

Medical Decision Making: How Patients Choose

Prescriptive models to support decision making in genetics.