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

PREAMBLE......e4

APPENDIX 1......e121

APPENDIX 2......e122

APPENDIX 3......e124

REFERENCES......e124

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

https://www.amjmed.com/article/0002-9343(86)90674-1/pdf

Postmenopausal Estrogens in Prevention of Osteoporosis Benefit Virtually without Risk if Cardiovascular Effects Are Considered

Introduction 3Part I 4Lifetime Patient-Centered Health Information Systems 4 The “Guardian Angel” Concept 4Architecture for “Guardian Angels” 5Guardian Angel Interactions and Interoperations 6Experimental Guardian Angel Applications 6Example Scenario 6Part II 9Objectives 9Architecture 9Reference Implementation 9Experimental Domains 10Experimental Testing 10Technical Rationale 11Roots of the Problems 11 a. Health information for patients. 11 b. Life-long information 11Domain Modeling 12Architecture 13Special Functionalities 15Generic Functional Modules 15Hardware Realizations 16Information Infrastructures and I-95 16 Prototype Applications 17Insulin-Dependent Juvenile Diabetes 17Chronic Hypertension 18Managing Hypertension Using a GA 19Chronic Anticoagulation 20Other Domains 22Implementation 22Comparison with Ongoing Research 23Performance Evaluation 24Conclusion 26 Schedule 26Part III .28Project Partners 28Previous Accomplishments 29MIT Laboratory for Computer Science 29 Tufts/NEMC 31Children’s Hospital 32Veterans Health Adm.: Boston Dev. Center 32 International Business Machines Corp 33Gensym Corporation 33Results, Products and Transferable Technology 34Bibliography 35

/pdf/guardian-angel-patient-centered-health-information-systems-1jgw21z115.pdf

Guardian Angel: Patient-Centered Health Information Systems

https://journal.chestnet.org/article/S0012-3692(15)31490-2/pdf

Applying the Grades of Recommendation for Antithrombotic and Thrombolytic Therapy : The Seventh ACCP Conference on Antithrombotic and Thrombolytic Therapy

Placenta accreta/percreta/increta: a cause of elevated maternal serum alpha-fetoprotein.

Comparative effectiveness is the evaluation of the relative (clinical) effectiveness, safety, and cost of 2 or more medical services, drugs, devices, therapies, or procedures used to treat the same condition. After reviewing comparative effectiveness efforts in this country and internationally, the American College of Physicians (1) concluded that the United States expends insufficient funds to develop comparative effectiveness data; that no coordination or prioritization of current efforts exists in either the public or private sector to produce comparative effectiveness information; and that the absence of readily available comparative effectiveness information interferes with the ability of physicians and their patients to make effective, informed treatment choices that meet the unique needs and preferences of the patient and facilitate the ability of payers to optimize the value of their health care expenditures. The College called for an adequately funded, trusted national entity to prioritize, sponsor, and produce comparative effectiveness and cost-effectiveness information. Others have recently made similar recommendations (27). The American College of Physicians recommended that this entity be protected from undue government and private sector influence, provide transparent proceedings and reports, include extensive stakeholder involvement, and implement processes to ensure the general distribution of comprehensible findings. Furthermore, the College recommended that the entity produce both comparative clinical and cost data, in the strong belief that both factors are critical to making health care resource decisions for all stakeholders. This article explores the availability and use of information that compares clinical outcomes and cost. It argues that the national entity should develop and use clinical information that compares the health care outcomes of plans of care, along with their costs, to ensure the most effective and efficient use of limited health care resources. Cost Information in Comparative Effectiveness Analyses Investigators use various terms and analytic techniques (such as cost-identification analysis, costconsequence analysis, and costbenefit analysis) when cost factors are a significant aspect in the comparison of the effectiveness of different medical interventions (8). The most frequently used technique, cost-effectiveness analysis (CEA), compares the incremental or marginal economic cost per unit of health care gained among different interventions for the same condition without attempting to monetize the health care gain. A cost-effectiveness analysis provides a single ratio, the incremental cost-effectiveness ratio, that reflects the difference in the costs of interventions (in U.S. dollars) divided by the difference in their health effectiveness or clinical outcomes. Health outcomes may be defined in various ways, such as life-years gained, cases of disease prevented, improved functional status, or quality of life. The measure recommended by the Panel on Cost-Effectiveness in Health and Medicine (9) is health-related quality-adjusted life-years (QALYs). A QALY assigns a weight ranging from 0 (death) to 1 (perfect health), corresponding to the patient's quality of life, for each period. Quality-adjusted life-years provide a common currency to assess the benefits or burdens that patients experience in terms of quality and quantity of life. Because this metric is a ratio, it can grow large either because its numerator (the incremental cost of the intervention) is large or, perhaps more important, because its denominator (the incremental effectiveness of the intervention) is small. Because the costs of an intervention have a finite limit, the largest incremental cost-effectiveness ratios (and hence the most inefficient uses of limited resources) occur when the more expensive intervention provides little or no health benefit. The Reluctance of Policymakers to Use Cost-Effectiveness The literature supports the development and use of evidence of relative clinical effectiveness; however, support for using cost-effectiveness data is more limited. This was most recently reflected in the Medicare Payment Advisory Commission's (MedPAC) June 2007 Report to Congress (4), which highlighted the need for increased comparative effectiveness information and recommended the development of a national entity to compare the clinical effectiveness of treatment approaches. Although the production of cost-effectiveness information was not ruled out, the MedPAC Commissioners showed substantial ambivalence about including such data during the public meeting where they reviewed this recommendation (10). Through its rulemaking process, Medicare has twice attempted to endorse the use of cost-effectiveness data in coverage decisions. On both occasions, strong, broadly based concern was expressed, including the fear that such use would be the forerunner to rationing of care (8), and Medicare abandoned its efforts. That policy directly affects traditional Medicare (Parts A and B) and private plan Medicare (Part C), which must provide at least the basic traditional benefits and must abide by all Medicare national coverage decisions that expand coverage (11). The new Medicare drug coverage benefit (Part D) provides greater flexibility for private drug plans to use costs in formulary decisions. Traditional state Medicaid programs are restricted in applying cost or cost-effectiveness data to their formularies (12). Medicaid programs must cover all drugs approved by the U.S. Food and Drug Administration from every manufacturer that signs an agreement with the Secretary of Health and Human Services to pay rebates to the states for the drugs purchased; however, preferred drug lists can be established if specific requirements are met. States do have the flexibility to employ cost and cost-effectiveness evidence in deciding whether to require prior authorization or other utilization management procedures. The Drug Effectiveness Review Project (www.ohsu.edu/drugeffectiveness) provides such information. State Medicaid programs are directly influenced by cost factors. State budgets go through cycles of surplus and deficit. During periods of deficit, coverage under Medicaid and other state-funded health programs is often reduced, typically by not covering specific populations or benefits that are not mandated under the specific program. Oregon attempted to use cost-effectiveness to prioritize Medicaid benefits in the early 1990s, but subsequently minimized the influence of costs on the prioritizing process after substantial stakeholder criticism (8). Only Washington has a program that formally assesses safety, efficacy, and cost-effectiveness to inform these decisions (13). Commercial health plans and purchasers are also reluctant to use formal CEA, even though 90% use costs in some form when evaluating new interventions (14). For example, the health insurance industry uses budgetary impact analyses, in which new technologies are evaluated in terms of their projected effect on cost per member per month, often without relating cost to patient outcomes (Weinstein MC. Personal communication). In a survey of private U.S. health plans, only 40% indicated that they use formal CEAs (14). In another survey, only 51% of private payers used CEAs or costbenefit analyses (15). Similarly, the Blue Cross Blue Shield Association's national Technology Evaluation Center generally excludes cost-effectiveness considerations (16). When cost-effectiveness data are used within the commercial sector, such use is typically limited to pharmaceutical coverage and pricing decisions. As summarized below, several reasons have been proposed for the observed reluctance to develop and use cost-effectiveness. Fears about Access to Services Patients and their advocates are concerned that use of any cost data, including formal CEAs, will inappropriately limit access, be used primarily for cost containment, and be a substantial step toward rationing of care (8, 17, 18). The reaction of Medicare's beneficiaries to the agency's attempts to allow limited consideration of cost-effectiveness is a good example. These fears are particularly strong in the United States, where the belief that the richest nation in the world should not consider cost in decisions about access to tests and treatment seems to persist (18). This opposition often focuses on cost-effectiveness data rather than cost data alone. Many insurers already use information about cost because they must manage finite budgets. Of note, the strongest opposition seems to come from such constituencies as elderly persons and patients with certain chronic illnesses, for whom the incremental benefits of expensive interventions are typically small. Opponents of the use of cost data neglect to recognize the unsustainable growth in health care expenditures. Medicare's trustees have expressed concerns over the past several years regarding the financial stability of the Medicare system (19). Furthermore, health care premium growth continues to far outpace growth in the economy and workers' earnings, making health care increasingly unaffordable to both employers and employees (20). Rationing is already occurring. More than 47 million people in the United States are uninsured for health care, primarily because they cannot afford the insurance premiums (21, 22). This lack of insurance decreases access to needed health care and produces substantially poorer medical outcomes (23). Health care resources are limited, even in this rich country, and cost already plays and will continue to play a role in health care expenditure decisions. Making valid and reliable cost-effectiveness data from a trusted source available to all stakeholders would provide a needed tool for both the general population and traditional health care purchasers (for example, government programs and employers). Such a tool could improve value an

Stephen G. Pauker

Papers

Postmenopausal Estrogens in Prevention of Osteoporosis Benefit Virtually without Risk if Cardiovascular Effects Are Considered

Guardian Angel: Patient-Centered Health Information Systems

Applying the Grades of Recommendation for Antithrombotic and Thrombolytic Therapy : The Seventh ACCP Conference on Antithrombotic and Thrombolytic Therapy

Placenta accreta/percreta/increta: a cause of elevated maternal serum alpha-fetoprotein.

Information on Cost-Effectiveness: An Essential Product of a National Comparative Effectiveness Program