Bøger af Mark Chang

Artificial intelligence (AI) is transforming the practice of medicine. It is helping doctors diagnose patients more accurately, predict treatment effects on individuals, and recommend better treatments. AI is also transforming the drug discovery and development process, helping pharmaceutical researchers to identify and design active drug candidates, and reducing the cost of the clinical testing phase. Recently, the FDA moved toward a new, tailored review framework for artificial intelligence-based medical devices (Gottlieb, April 2019).This book is intended for a broad readership: sufficiently straightforward for college freshmen and informative enough for researchers. Chapter 1 gives a gentle introduction to the five ML categories of learning: supervised, unsupervised, reinforcement, evolutionary and swarm intelligence. Chapters 2 through 6 discuss the key concepts of the main methods in each of the five AI categories and their applications in pharmaceutical research & development and healthcare. Chapter 7 provides a state-of-the-art review of AI applications in prescription drug discovery, development, pharmacovigilance, and healthcare. Chapter 8 discusses artificial general intelligence and its controversies, challenges, and likely future directions. A few equations are included to effectively deliver key concepts and 100 key references are cited to meet researchers' needs. The book is a simplified version of my previous book: Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare. Readers who want to get hands on experiences may explore the book with computer code in R.

Humanized AI (HAI), emerging as the next of the AI waves, refers to artificial social beings that are very close to humans in various aspects, beings who are machine-race humans, not digital slaves. Foundation, Architecture and Prototyping of Humanized AI deploys a novel small-data approach to vertically explore the spectrum of HAI.

This book introduces modern methods in biostatistics and helps researchers and students quickly grasp key concepts and methods. It details novel ingredients or developments in methodology, computation algorithms and applications.

All the Essentials to Start Using Adaptive Designs in No TimeCompared to traditional clinical trial designs, adaptive designs often lead to increased success rates in drug development at reduced costs and time. Introductory Adaptive Trial Designs: A Practical Guide with R motivates newcomers to quickly and easily grasp the essence of adaptive designs as well as the foundations of adaptive design methods.The book reduces the mathematics to a minimum and makes the material as practical as possible. Instead of providing general, black-box commercial software packages, the author includes open-source R functions that enable readers to better understand the algorithms and customize the designs to meet their needs. Readers can run the simulations for all the examples and change the input parameters to see how each input parameter affects the simulation outcomes or design operating characteristics.Taking a learning-by-doing approach, this tutorial-style book guides readers on planning and executing various types of adaptive designs. It helps them develop the skills to begin using the designs immediately.

Helping you become a skillful "simulator," this book provides broad coverage of the drug development process, from drug discovery to preclinical and clinical trial aspects to commercialization. It presents the theories and methods for carrying out computer simulations efficiently, covers both descriptive and pseudocode algorithms that provide the basis for implementing the simulation methods, and illustrates real-world problems through case studies. The author discusses many key topics, including game theory, adaptive design, molecular design, prescription drug marketing, biological pathway simulation, genetic programming, and pharmacokinetic modeling.

Get Up to Speed on Many Types of Adaptive Designs Since the publication of the first edition, there have been remarkable advances in the methodology and application of adaptive trials. Incorporating many of these new developments, Adaptive Design Theory and Implementation Using SAS and R, Second Edition offers a detailed framework to understand the use of various adaptive design methods in clinical trials. New to the Second Edition Twelve new chapters covering blinded and semi-blinded sample size reestimation design, pick-the-winners design, biomarker-informed adaptive design, Bayesian designs, adaptive multiregional trial design, SAS and R for group sequential design, and much more More analytical methods for K-stage adaptive designs, multiple-endpoint adaptive design, survival modeling, and adaptive treatment switching New material on sequential parallel designs with rerandomization and the skeleton approach in adaptive dose-escalation trials Twenty new SAS macros and R functions Enhanced end-of-chapter problems that give readers hands-on practice addressing issues encountered in designing real-life adaptive trials Covering even more adaptive designs, this book provides biostatisticians, clinical scientists, and regulatory reviewers with up-to-date details on this innovative area in pharmaceutical research and development. Practitioners will be able to improve the efficiency of their trial design, thereby reducing the time and cost of drug development.

Taking a learning-by-doing approach, this tutorial-style book helps newcomers quickly and easily grasp the essence of adaptive designs as well as the foundations of adaptive design methods. It reduces the mathematics to a minimum and makes the material as practical as possible. The author includes R functions that enable readers to better understand the algorithms and customize the designs to meet their needs. For those with no R experience, an appendix provides a 30-minute R tutorial.

The author uses concrete examples to explain fundamental principles behind scientific research and analogies to arrive at a general principle or common notion. The scientific method is used to understand how and why things happen, make predictions, prevent mistakes, and solve problems.

The paradoxology of scientific inference refers to the study of the nature of scientific inference and evidence through controversies or, more precisely, paradoxes. This book analyzes paradoxes from many different perspectives, such as statistics, mathematics, philosophy, science, and artificial intelligence. It compares various quantitative methods of evidence measures, including frequentist hypothesis testing, likelihood, and Bayesian methods. The text elaborates on recent findings to reach new and exciting conclusions. Other topics covered include probability, plausible reasoning, multiplicity, and inferences.

Helping you become a creative, logical thinker and skillful 'simulator,' this book provides coverage of the entire drug development process, from drug discovery to preclinical and clinical trial aspects to commercialization. It presents the theories and methods needed to carry out computer simulations efficiently.

This book with CD-ROM introduces the Software ExpDesign Studio for classic and adaptive designs and computer simulations. The book includes step-by-step demos on creating various trial designs, using the automatic outputs to optimize the trial design, and monitoring a clinical trial in progress.