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,

/pdf/acc-aha-2006-guidelines-for-the-management-of-patients-with-27qfh9fc7i.pdf

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

/pdf/grading-quality-of-evidence-and-strength-of-recommendations-4juchvwoau.pdf

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

Preventing sudden cardiac death. Authors' reply

In Reply. —Economic analyses of health care strategies must carefully differentiate costs from charges.1 Costs represent the actual amount paid for resources consumed in providing a service, while charges represent the price requested for reimbursement of that service. Because market forces affect charges, large and seemingly arbitrary differences between costs and charges for different services may exist.2 We agree with Dr Makler that it is important to use costs, which correspond more closely to actual resource consumption, and not charges, when performing cost-effectiveness analyses.Makler believes that the costs listed in our analysis are not representative, quoting the 1993 Medicare resource-based relative value scale—based reimbursement for a three-phase bone scan of $204.06 as proof. We believe this is an underestimate, which may include neither the costs of the radiopharmaceutical nor the hospital costs for the addition of a three-phase scan to the regional scan. Unfortunately, even costs vary

Cost-effectiveness of Noninvasive Testing for Osteomyelitis-Reply

Kidney Failure or Cancer

Robert S. Ledley, one of the founding fathers of the field of medical decision making and the man who applied computer science to medical judgment and diagnosis, died July 24, 2012, after a prolonged struggle with Alzheimer’s. Our community, as well as the medical field as a whole, has lost a pioneering genius. Bob Ledley’s legacy will live on through his magnum opus ‘‘Reasoning Foundations of Medical Diagnosis,’’ coauthored with Lee B. Lusted, MD, FACMI. Lee Lusted was one of the founders of the Society for Medical Decision Making and the founding editor of Medical Decision Making. In their seminal work, the authors showed how computers could help physicians with diagnoses and even taught medical workers how to use edge-notched cards to create diagnostic databases. Most important, this article addressed decision making under uncertainty and valuing outcomes. Citation analyses have demonstrated that this paper lies at the root of medical decision science. In fact, many readers may recall their introduction to our discipline through this article. We now explore the rest of his talent for technological innovation. Robert Steven Ledley was born in Flushing Meadows, New York, in 1926. While attending dental school at New York University College (at his parents’ strong behest, so that he could earn a living), he also pursued his interest in physics at Columbia University. He received his DDS in 1948 and his MS in theoretical physics the following year. One of his professors ‘‘joked that he was the only physicist who could pull a man’s tooth.’’ When the Korean War broke out, Ledley volunteered as a First Lieutenant in the US Army Dental Corps; as a result of his background in physics, he was assigned to research prosthetic dental devices at Walter Reed Army Medical Center in Washington, DC. His work optimizing denture fitting based on ‘‘angle of chew’’ earned him an article from the Associated Press titled ‘‘Mathematics Used to Keep False Teeth in Place.’’ After completing his service, Bob served as an investigator at the National Bureau of Standards and Johns Hopkins University and was an associate professor at George Washington University. While at the National Bureau of Standards, Ledley obtained for his wife (the former Terry Wachtell, a music major whom he convinced to switch to mathematics) a position as a programmer on the Standards Eastern Automatic Computer (SEAC). Bob was fascinated and spent a decade learning to program before using the computer to solve complex problems in his own research. He was an ardent believer in the prowess of computational research: ‘‘I had previously realized that although, conceptually, physics equations could be written to describe any biomedical phenomenon, such equations would be so complex that they could not feasibly be solved in closed form. Thus SEAC would be my panacea, because the equations would become tractable to numerical methods of solutions. Or so I truly believed at the time. That was to be my field, application of computers to biomedical problems.’’ It didn’t take long for Ledley to arrive at a natural starting point—computerizing the diagnostic process. He recalled, ‘‘I put together a deck of the McBee keysort cards (the cards with the holes around the margins) for diseases of the tongue. Each card was a disease, and symptoms related to the disease of the card were punched out to the card margin. Then, if needles were pushed through the edge holes From University of Illinois at Chicago, Chicago, IL (SS); Fox Chase Cancer Center, Philadelphia, PA (JRB); and Tufts Medical Center, Boston, MA (SGP).

https://mdm.sagepub.com/content/33/5/731.full.pdf

Stephen G. Pauker

Papers

Preventing sudden cardiac death. Authors' reply

Cost-effectiveness of Noninvasive Testing for Osteomyelitis-Reply

Kidney Failure or Cancer

In memoriam: Robert Steven Ledley, DDS, MS (physics), 1926-2012.

Survivor Costs in Cost-Effectiveness Analysis