Predicting Potential Pitfalls
Predicting Potential Pitfalls

Author: Jordan Lee Watts

Publisher:

Published: 2017

Total Pages: 60

ISBN-13:

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Evidence shows that identifying potential consequences as a part of ethical dilemma assessment leads to improved forecasting and greater decision ethicality when one is deciding which actions to pursue. The present experiments attempt to expand upon past research and examine the potential impact deliberate identification of consequences has on the assessment of another individual's unethical response to a dilemma. The usefulness of this strategy as a training tool is assessed. Finally, the experiments examine how deliberate consequence identification may interact with previously reported effects of cognitive load. In Study 1, the deliberate identification of consequences was found to result in significant differences on ethical dilemma assessment. Those who were asked to identify consequences that could occur from unethical actions predicted significantly more harm as a result of those actions compared to controls. Additionally, these participants reported that the unethical actions were wrong and indicated an unwillingness to take the same actions with significantly more conviction compared to controls. Study 2 attempted to examine whether or not participants who successfully used the consequence identification procedure previously would display persistent positive effects on future dilemma assessment when they were not required to deliberately identify consequences. In the initial training session, participants were asked to identify either four or eight consequences on two scenarios. Significant differences were observed compared to controls on measures of ethical judgment and ethical intentions. Those asked to identify consequences displayed stronger conviction that unethical actions were wrong and reported less willingness to take the same actions. No significant differences were observed between those who underwent consequence identification training and those who did not on measures of harm prediction, ethical judgments, or ethical intentions when assessing dilemma scenarios immediately after practice or one week later. Study 3 attempted to further expand these findings by combining consequence identification with previous research on cognitive load. Significant differences were observed between those asked to identify consequences and controls on measures of predicted harm, ethical judgments, and ethical intentions. However, no significant differences were observed as a result of the cognitive load manipulation. Implications of these findings and future directions are discussed.

Fundamentals of Clinical Data Science
Fundamentals of Clinical Data Science

Author: Pieter Kubben

Publisher: Springer

Published: 2018-12-21

Total Pages: 219

ISBN-13: 3319997130

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This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

Leveraging Data Science for Global Health
Leveraging Data Science for Global Health

Author: Leo Anthony Celi

Publisher: Springer Nature

Published: 2020-07-31

Total Pages: 471

ISBN-13: 3030479943

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This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.

Clinical Prediction Models
Clinical Prediction Models

Author: Ewout W. Steyerberg

Publisher: Springer

Published: 2019-07-22

Total Pages: 574

ISBN-13: 3030163997

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The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: • A discussion of Big Data and its implications for the design of prediction models • Machine learning issues • More simulations with missing ‘y’ values • Extended discussion on between-cohort heterogeneity • Description of ShinyApp • Updated LASSO illustration • New case studies

Predicting Presidential Elections and Other Things
Predicting Presidential Elections and Other Things

Author: Ray C. Fair

Publisher: Stanford University Press

Published: 2002

Total Pages: 196

ISBN-13: 9780804745093

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What do the following events have in common? In 2000, the election between George W. Bush and Al Gore was a virtual tie. The 1989 and 1990 vintages have turned out to be two of the best ever for Bordeaux wines. In 2001, the Federal Reserve lowered the interest rate eleven times. The decade of the 1970s was one of the worst on record for U.S. inflation. In 2001, the author of this book, at age 59, ran a marathon in 3 hours and 30 minutes, but should have been able to do it in 3 hours and 15 minutes. This book shows clearly and simply how these diverse events can be explained by using the tools of the social sciences and statistics. It moves from a discussion of formulating theories about real world phenomena to lessons on how to analyze data, test theories, and make predictions. Through the use of a rich array of examples, the book demonstrates the power and range of social science and statistical methods. In addition to “big” topics—presidential elections, Federal Reserve behavior, and inflation—and “not quite so big” topics—wine quality—the book takes on questions of more direct, personal interest. Who of your friends is most likely to have an extramarital affair? How important is class attendance for academic performance in college? How fast can you expect to run a race or perform some physical task at age 55, given your time at age 30? (In other words, how fast are you slowing down?) As the author works his way through an incredibly broad range of questions and topics, demonstrating the usefulness of statistical theory and method, he gives the reader a new way of thinking about many age-old concerns in public and private life.

Predicting, Monitoring, and Assessing Forest Fire Dangers and Risks
Predicting, Monitoring, and Assessing Forest Fire Dangers and Risks

Author: Baranovskiy, Nikolay Viktorovich

Publisher: IGI Global

Published: 2019-12-27

Total Pages: 417

ISBN-13: 1799818691

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To understand the catastrophic processes of forest fire danger, different deterministic, probabilistic, and empiric models must be used. Simulating various surface and crown forest fires using predictive information technology could lead to the improvement of existing systems and the examination of the ecological and economic effects of forest fires in other countries. Predicting, Monitoring, and Assessing Forest Fire Dangers and Risks provides innovative insights into forestry management and fire statistics. The content within this publication examines climate change, thermal radiation, and remote sensing. It is designed for fire investigators, forestry technicians, emergency managers, fire and rescue specialists, professionals, researchers, meteorologists, computer engineers, academicians, and students invested in topics centered around providing conjugate information on forest fire danger and risk.

Handbook of Choice Modelling
Handbook of Choice Modelling

Author: Stephane Hess

Publisher: Edward Elgar Publishing

Published: 2024-06-05

Total Pages: 797

ISBN-13: 1800375638

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This thoroughly revised second edition Handbook provides an authoritative and in-depth overview of choice modelling, covering essential topics range from data collection through model specification and estimation to analysis and use of results. It aptly emphasises the broad relevance of choice modelling when applied to a multitude of fields, including but not limited to transport, marketing, health and environmental economics.

Global Catastrophes and Trends
Global Catastrophes and Trends

Author: Vaclav Smil

Publisher: MIT Press

Published: 2012-09-14

Total Pages: 323

ISBN-13: 0262518228

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A wide-ranging, interdisciplinary look at global changes that may occur over the next fifty years—whether sudden and cataclysmic world-changing events or gradually unfolding trends. Fundamental change occurs most often in one of two ways: as a “fatal discontinuity,” a sudden catastrophic event that is potentially world changing, or as a persistent, gradual trend. Global catastrophes include volcanic eruptions, viral pandemics, wars, and large-scale terrorist attacks; trends are demographic, environmental, economic, and political shifts that unfold over time. In this provocative book, scientist Vaclav Smil takes a wide-ranging, interdisciplinary look at the catastrophes and trends the next fifty years may bring. Smil first looks at rare but cataclysmic events, both natural and human-produced, then at trends of global importance, including the transition from fossil fuels to other energy sources and growing economic and social inequality. He also considers environmental change—in some ways an amalgam of sudden discontinuities and gradual change—and assesses the often misunderstood complexities of global warming. Global Catastrophes and Trends does not come down on the side of either doom-and-gloom scenarios or techno-euphoria. Instead, Smil argues that understanding change will help us reverse negative trends and minimize the risk of catastrophe.

Practical Statistics for Data Scientists
Practical Statistics for Data Scientists

Author: Peter Bruce

Publisher: "O'Reilly Media, Inc."

Published: 2017-05-10

Total Pages: 322

ISBN-13: 1491952911

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Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data