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Teaching the fundamental ideas of decision analysis, this text avoids an overly technical explanation of the mathematics used in management science. This new version incorporates and implements the powerful Decision Tools Suite, a toolkit for risk and decision analysis.

This new edition includes some key topics relating to the latest version of MS Office, including use of the ribbon, current Excel file types, Dashboard, and basic Sharepoint integration. It shows how to automate operations, such as curve fitting, sorting, filtering, and analyzing data from a variety of sources. The book allows users to analyze data and automate the preparation of custom reports and demonstrates how to assign Excel VBA code to the new “Ribbon” user interface.

Professional-level coverage and techniques for Excel power users Aimed at Excel power users who appreciate logical, clean explanations of techniques, this visual guide features numerous screenshots and easy-to-follow numbered steps in order to show you how to perform professional-level modeling, charting, data sharing, data access, data slicing, and other functions. You'll find super techniques for getting the most out of Excel's statistical and financial functions, Excel PivotTables and PivotCharts, Excel Solver, and more. Demonstrates how to crunch and analyze Excel data the way the professionals do in an uncluttered, visual style Offers a clear look at power-using the new Excel 2013, the latest version of the world's leading spreadsheet application from Microsoft Expands your Excel knowledge and helps you use Excel data more efficiently Explains how to retrieve data from databases; cut, slice, and pivot data using PivotTables; model data and chart data; and use advanced formulas Explores all features and functions in two-color pages packed with screenshots, numbered steps, and other visual graphics that clearly show you how to accomplish tasks Includes practical examples, tips, and advice to help you get the most out of Excel's features and functions Learn the full power of Excel 2013 with this helpful guide!.

Professional-level coverage and techniques for Excel power users Aimed at Excel power users who appreciate logical, clean explanations of techniques, this visual guide features numerous screenshots and easy-to-follow numbered steps in order to show you how to perform professional-level modeling, charting, data sharing, data access, data slicing, and other functions. You'll find super techniques for getting the most out of Excel's statistical and financial functions, Excel PivotTables and PivotCharts, Excel Solver, and more. Demonstrates how to crunch and analyze Excel data the way the professionals do in an uncluttered, visual style Offers a clear look at power-using the new Excel 2013, the latest version of the world's leading spreadsheet application from Microsoft Expands your Excel knowledge and helps you use Excel data more efficiently Explains how to retrieve data from databases; cut, slice, and pivot data using PivotTables; model data and chart data; and use advanced formulas Explores all features and functions in two-color pages packed with screenshots, numbered steps, and other visual graphics that clearly show you how to accomplish tasks Includes practical examples, tips, and advice to help you get the most out of Excel's features and functions Learn the full power of Excel 2013 with this helpful guide!.

Background Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients’ disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning models and high dimensional data sources such as electronic health records, magnetic resonance imaging scans, cardiotocograms, etc. These foundations make interpretation and explanation of the CAD advice very challenging. This challenge is recognised throughout the machine learning research community. eXplainable Artificial Intelligence (XAI) is emerging as one of the most important research areas of recent years because it addresses the interpretability and trust concerns of critical decision makers, including those in clinical and medical practice. Methods In this work, we focus on AdaBoost, a black box model that has been widely adopted in the CAD literature. We address the challenge – to explain AdaBoost classification – with a novel algorithm that extracts simple, logical rules from AdaBoost models. Our algorithm, Adaptive-Weighted High Importance Path Snippets (Ada-WHIPS), makes use of AdaBoost’s adaptive classifier weights. Using a novel formulation, Ada-WHIPS uniquely redistributes the weights among individual decision nodes of the internal decision trees of the AdaBoost model. Then, a simple heuristic search of the weighted nodes finds a single rule that dominated the model’s decision. We compare the explanations generated by our novel approach with the state of the art in an experimental study. We evaluate the derived explanations with simple statistical tests of well-known quality measures, precision and coverage, and a novel measure stability that is better suited to the XAI setting. Results Experiments on 9 CAD-related data sets showed that Ada-WHIPS explanations consistently generalise better (mean coverage 15%-68%) than the state of the art while remaining competitive for specificity (mean precision 80%-99%). A very small trade-off in specificity is shown to guard against over-fitting which is a known problem in the state of the art methods. Conclusions The experimental results demonstrate the benefits of using our novel algorithm for explaining CAD AdaBoost classifiers widely found in the literature. Our tightly coupled, AdaBoost-specific approach outperforms model-agnostic explanation methods and should be considered by practitioners looking for an XAI solution for this class of models.

Lung cancer screening is a US Preventive Services Task Force Grade B recommendation that has been shown to decrease lung cancer-related mortality by approximately 20%. However, making the decision to screen, or not, for lung cancer is a complex decision because there are potential risks (eg, false positive results, overdiagnosis). Shared decision making was incorporated into the lung cancer screening guideline and, for the first time, is a requirement for reimbursement of a cancer screening test from Medicare. Awareness of lung cancer screening remains low in both the general and screening-eligible populations. When a screening-eligible person visits their clinician never having heard about lung cancer screening, engaging in shared decision making to arrive at an informed decision can be a challenge. Methods to effectively prepare patients for these clinical encounters and support both patients and clinicians to engage in these important discussions are needed. The aim of the study was to estimate the effects of a computer-tailored decision support tool that meets the certification criteria of the International Patient Decision Aid Standards that will prepare individuals and support shared decision making in lung cancer screening decisions. A pilot randomized controlled trial with a community-based sample of 60 screening-eligible participants who have never been screened for lung cancer was conducted. Approximately half of the participants (n=31) were randomized to view LungTalk-a web-based tailored computer program-while the other half (n=29) viewed generic information about lung cancer screening from the American Cancer Society. The outcomes that were compared included lung cancer and screening knowledge, lung cancer screening health beliefs (perceived risk, perceived benefits, perceived barriers, and self-efficacy), and perception of being prepared to engage in a discussion about lung cancer screening with their clinician. Knowledge scores increased significantly for both groups with greater improvement noted in the group receiving LungTalk (2.33 vs 1.14 mean change). Perceived self-efficacy and perceived benefits improved in the theoretically expected directions. LungTalk goes beyond other decision tools by addressing lung health broadly, in the context of performing a low-dose computed tomography of the chest that has the potential to uncover other conditions of concern beyond lung cancer, to more comprehensively educate the individual, and extends the work of nontailored decision aids in the field by introducing tailoring algorithms and message framing based upon smoking status in order to determine what components of the intervention drive behavior change when an individual is informed and makes the decision whether to be screened or not to be screened for lung cancer. RR2-10.2196/resprot.8694.

Continuous electronic fetal heart-rate monitoring is widely used during labour, and computerised interpretation could increase its usefulness. We aimed to establish whether the addition of decision-support software to assist in the interpretation of cardiotocographs affected the number of poor neonatal outcomes. In this unmasked randomised controlled trial, we recruited women in labour aged 16 years or older having continuous electronic fetal monitoring, with a singleton or twin pregnancy, and at 35 weeks' gestation or more at 24 maternity units in the UK and Ireland. They were randomly assigned (1:1) to decision support with the INFANT system or no decision support via a computer-generated stratified block randomisation schedule. The primary outcomes were poor neonatal outcome (intrapartum stillbirth or early neonatal death excluding lethal congenital anomalies, or neonatal encephalopathy, admission to the neonatal unit within 24 h for ≥48 h with evidence of feeding difficulties, respiratory illness, or encephalopathy with evidence of compromise at birth), and developmental assessment at age 2 years in a subset of surviving children. Analyses were done by intention to treat. This trial is completed and is registered with the ISRCTN Registry, number 98680152. Between Jan 6, 2010, and Aug 31, 2013, 47 062 women were randomly assigned (23 515 in the decision-support group and 23 547 in the no-decision-support group) and 46 042 were analysed (22 987 in the decision-support group and 23 055 in the no-decision-support group). We noted no difference in the incidence of poor neonatal outcome between the groups—172 (0·7%) babies in the decision-support group compared with 171 (0·7%) babies in the no-decision-support group (adjusted risk ratio 1·01, 95% CI 0·82–1·25). At 2 years, no significant differences were noted in terms of developmental assessment. Use of computerised interpretation of cardiotocographs in women who have continuous electronic fetal monitoring in labour does not improve clinical outcomes for mothers or babies. National Institute for Health Research.