Download or read book Machine Learning in Medicine Cookbook Two written by Ton J. Cleophas and published by . This book was released on 2014-06-30 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning in Medicine Cookbook written by Ton J. Cleophas and published by Springer Science & Business Media. This book was released on 2014-01-03 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: The amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional methods of data analysis have difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning and the current 100 page cookbook should be helpful to that aim. It covers in a condensed form the subjects reviewed in the 750 page three volume textbook by the same authors, entitled “Machine Learning in Medicine I-III” (ed. by Springer, Heidelberg, Germany, 2013) and was written as a hand-hold presentation and must-read publication. It was written not only to investigators and students in the fields, but also to jaded clinicians new to the methods and lacking time to read the entire textbooks. General purposes and scientific questions of the methods are only briefly mentioned, but full attention is given to the technical details. The two authors, a statistician and current president of the International Association of Biostatistics and a clinician and past-president of the American College of Angiology, provide plenty of step-by-step analyses from their own research and data files for self-assessment are available at extras.springer.com. From their experience the authors demonstrate that machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.

Download or read book Machine Learning in Medicine Cookbook Two written by Ton J. Cleophas and published by Springer. This book was released on 2014-05-27 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: The amount of data medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Consequently, proper data-based health decisions will soon be impossible. Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning methods and this was the main incentive for the authors to complete a series of three textbooks entitled “Machine Learning in Medicine Part One, Two and Three, Springer Heidelberg Germany, 2012-2013", describing in a nonmathematical way over sixty machine learning methodologies, as available in SPSS statistical software and other major software programs. Although well received, it came to our attention that physicians and students often lacked time to read the entire books, and requested a small book, without background information and theoretical discussions and highlighting technical details. For this reason we produced a 100 page cookbook, entitled "Machine Learning in Medicine - Cookbook One", with data examples available at extras.springer.com for self-assessment and with reference to the above textbooks for background information. Already at the completion of this cookbook we came to realize, that many essential methods were not covered. The current volume, entitled "Machine Learning in Medicine - Cookbook Two" is complementary to the first and also intended for providing a more balanced view of the field and thus, as a must-read not only for physicians and students, but also for any one involved in the process and progress of health and health care. Similarly to Machine Learning in Medicine - Cookbook One, the current work will describe stepwise analyses of over twenty machine learning methods, that are, likewise, based on the three major machine learning methodologies: Cluster methodologies (Chaps. 1-3) Linear methodologies (Chaps. 4-11) Rules methodologies (Chaps. 12-20) In extras.springer.com the data files of the examples are given, as well as XML (Extended Mark up Language), SPS (Syntax) and ZIP (compressed) files for outcome predictions in future patients. In addition to condensed versions of the methods, fully described in the above three textbooks, an introduction is given to SPSS Modeler (SPSS' data mining workbench) in the Chaps. 15, 18, 19, while improved statistical methods like various automated analyses and Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8. We should emphasize that all of the methods described have been successfully applied in practice by the authors, both of them professors in applied statistics and machine learning at the European Community College of Pharmaceutical Medicine in Lyon France. We recommend the current work not only as a training companion to investigators and students, because of plenty of step by step analyses given, but also as a brief introductory text to jaded clinicians new to the methods. For the latter purpose, background and theoretical information have been replaced with the appropriate references to the above textbooks, while single sections addressing "general purposes", "main scientific questions" and "conclusions" are given in place. Finally, we will demonstrate that modern machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.

Download or read book Machine Learning in Medicine Cookbook Three written by Ton J. Cleophas and published by Springer. This book was released on 2014-10-29 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unique features of the book involve the following. 1.This book is the third volume of a three volume series of cookbooks entitled "Machine Learning in Medicine - Cookbooks One, Two, and Three". No other self-assessment works for the medical and health care community covering the field of machine learning have been published to date. 2. Each chapter of the book can be studied without the need to consult other chapters, and can, for the readership's convenience, be downloaded from the internet. Self-assessment examples are available at extras.springer.com. 3. An adequate command of machine learning methodologies is a requirement for physicians and other health workers, particularly now, because the amount of medical computer data files currently doubles every 20 months, and, because, soon, it will be impossible for them to take proper data-based health decisions without the help of machine learning. 4. Given the importance of knowledge of machine learning in the medical and health care community, and the current lack of knowledge of it, the readership will consist of any physician and health worker. 5. The book was written in a simple language in order to enhance readability not only for the advanced but also for the novices. 6. The book is multipurpose, it is an introduction for ignorant, a primer for the inexperienced, and a self-assessment handbook for the advanced. 7. The book, was, particularly, written for jaded physicians and any other health care professionals lacking time to read the entire series of three textbooks. 8. Like the other two cookbooks it contains technical descriptions and self-assessment examples of 20 important computer methodologies for medical data analysis, and it, largely, skips the theoretical and mathematical background. 9. Information of theoretical and mathematical background of the methods described are displayed in a "notes" section at the end of each chapter. 10.Unlike traditional statistical methods, the machine learning methodologies are able to analyze big data including thousands of cases and hundreds of variables. 11. The medical and health care community is little aware of the multidimensional nature of current medical data files, and experimental clinical studies are not helpful to that aim either, because these studies, usually, assume that subgroup characteristics are unimportant, as long as the study is randomized. This is, of course, untrue, because any subgroup characteristic may be vital to an individual at risk. 12. To date, except for a three volume introductary series on the subject entitled "Machine Learning in Medicine Part One, Two, and Thee, 2013, Springer Heidelberg Germany" from the same authors, and the current cookbook series, no books on machine learning in medicine have been published. 13. Another unique feature of the cookbooks is that it was jointly written by two authors from different disciplines, one being a clinician/clinical pharmacologist, one being a mathematician/biostatistician. 14. The authors have also jointly been teaching at universities and institutions throughout Europe and the USA for the past 20 years. 15. The authors have managed to cover the field of medical data analysis in a nonmathematical way for the benefit of medical and health workers. 16. The authors already successfully published many statistics textbooks and self-assessment books, e.g., the 67 chapter textbook entitled "Statistics Applied to Clinical Studies 5th Edition, 2012, Springer Heidelberg Germany" with downloads of 62,826 copies. 17. The current cookbook makes use, in addition to SPSS statistical software, of various free calculators from the internet, as well as the Konstanz Information Miner (Knime), a widely approved free machine learning package, and the free Weka Data Mining package from New Zealand. 18. The above software packages with hundreds of nodes, the basic processing units including virtually all of the statistical and data mining methods, can be used not only for data analyses, but also for appropriate data storage. 19. The current cookbook shows, particularly, for those with little affinity to value tables, that data mining in the form of a visualization process is very well feasible, and often more revealing than traditional statistics. 20.The Knime and Weka data miners uses widely available excel data files. 21. In current clinical research prospective cohort studies are increasingly replacing the costly controlled clinical trials, and modern machine learning methodologies like probit and tobit regressions as well as neural networks, Bayesian networks, and support vector machines prove to better fit their analysis than traditional statistical methods do. 22. The current cookbook not only includes concise descriptions of standard machine learning methods, but also of more recent methods like the linear machine learning models using ordinal and loglinear regression. 23. Machine learning tends to increasingly use evolutionary operation methodologies. Also this subject has been covered. 24. All of the methods described have been applied in the authors' own research prior to this publication.

Download or read book Machine Learning in Medicine A Complete Overview written by Ton J. Cleophas and published by Springer Nature. This book was released on 2020-03-03 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adequate health and health care is no longer possible without proper data supervision from modern machine learning methodologies like cluster models, neural networks, and other data mining methodologies. The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector, and it was written as a training companion, and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care. In this second edition the authors have removed the textual errors from the first edition. Also, the improved tables from the first edition, have been replaced with the original tables from the software programs as applied. This is, because, unlike the former, the latter were without error, and readers were better familiar with them. The main purpose of the first edition was, to provide stepwise analyses of the novel methods from data examples, but background information and clinical relevance information may have been somewhat lacking. Therefore, each chapter now contains a section entitled "Background Information". Machine learning may be more informative, and may provide better sensitivity of testing than traditional analytic methods may do. In the second edition a place has been given for the use of machine learning not only to the analysis of observational clinical data, but also to that of controlled clinical trials. Unlike the first edition, the second edition has drawings in full color providing a helpful extra dimension to the data analysis. Several machine learning methodologies not yet covered in the first edition, but increasingly important today, have been included in this updated edition, for example, negative binomial and Poisson regressions, sparse canonical analysis, Firth's bias adjusted logistic analysis, omics research, eigenvalues and eigenvectors.

Download or read book Paging Dr Within written by Tom Garz and published by Tom Garz - TG Ideas LLC. This book was released on 2020-03-22 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a Patient - Would you like a "Patient Listener"? Are you tired of Medicine/Treatment "Trial and Error", at your expense? Are you tired of being shuffled from one Doctor to another? Do you want to "get better" and "stay better"? As a Doctor – Would you like more good information from Patients, relevant to their symptoms? Would you like to help Patients "get better" and "stay better"? Are you open to venturing out of your "comfort zone" in diagnosing and treating patients? Do you sometimes wonder if there is more to diagnosing/treating than what you were taught in Medical School? As a Health/Medical Innovator, Inventor, Engineer, Writer, other Creative Person – Are you looking for some new ideas? Would you like to "Interface" with the "Dr. Within" each of us? As an Insurance Company - Would you like to "pay out" less? If you answered yes to any of the above, maybe this book is for you. This book describes the Concepts of a "Patient Listener" and a "Super Symptom Checker" – Human, Computer, and/or Computer-Assisted Human – Considering the "Big Picture" around Health and/or Symptoms. This book is about 250 pages, a little over half written text. The remainder contains many Reference Links, from which you can build upon and learn from. The author of this book has set up Discussion Groups for this book to help others share, network, collaborate, etc. *** Use of the Information in this book may help the Patient, Doctor, and/or Others "Get Better". Some common Side Effects may include: A better understanding of what affects Health and Symptoms, Seeing the big picture surrounding Symptoms, Better health, less dependence on medication/treatment, generally "feeling better", Experiencing less perceived stress, more contentment with self and life, Perceiving more control of your life, in general, realizing there are always options no matter what, New insights on what could be done to make "it" better. Note - Continued Use of the Information in this book may result in "Staying Better". Ask your Doctor if "Getting Better" and "Staying Better" are right for you. :-)

Download or read book Machine Learning in Medicine written by Ton J. Cleophas and published by Springer Science & Business Media. This book was released on 2013-05-30 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, and fuzzy modeling. This second volume includes various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, genetic programming, association rule learning, anomaly detection, correspondence analysis, and other subjects. Both the theoretical bases and the step by step analyses are described for the benefit of non-mathematical readers. Each chapter can be studied without the need to consult other chapters. Traditional statistical tests are, sometimes, priors to machine learning methods, and they are also, sometimes, used as contrast tests. To those wishing to obtain more knowledge of them, we recommend to additionally study (1) Statistics Applied to Clinical Studies 5th Edition 2012, (2) SPSS for Starters Part One and Two 2012, and (3) Statistical Analysis of Clinical Data on a Pocket Calculator Part One and Two 2012, written by the same authors, and edited by Springer, New York.

Download or read book Quality Assurance in the Era of Individualized Medicine written by Moumtzoglou, Anastasius S. and published by IGI Global. This book was released on 2019-11-29 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is a significant deficiency among contemporary medicine practices reflected by experts making medical decisions for a large proportion of the population for which no or minimal data exists. Fortunately, our capacity to procure and apply such information is rapidly rising. As medicine becomes more individualized, the implementation of health IT and data interoperability become essential components to delivering quality healthcare. Quality Assurance in the Era of Individualized Medicine is a collection of innovative research on the methods and utilization of digital readouts to fashion an individualized therapy instead of a mass-population-directed strategy. While highlighting topics including assistive technologies, patient management, and clinical practices, this book is ideally designed for health professionals, doctors, nurses, hospital management, medical administrators, IT specialists, data scientists, researchers, academicians, and students.

Download or read book Fundamentals of Machine Learning and Deep Learning in Medicine written by Reza Borhani and published by Springer Nature. This book was released on 2022-11-18 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mathematics but yearn for a better understanding of this disruptive technology and its impact on medicine. Once an esoteric subject known to few outside of computer science and engineering departments, today artificial intelligence (AI) is a widely popular technology used by scholars from all across the academic universe. In particular, recent years have seen a great deal of interest in the AI subfields of machine learning and deep learning from researchers in medicine and life sciences, evidenced by the rapid growth in the number of articles published on the topic in peer-reviewed medical journals over the last decade. The demand for high-quality educational resources in this area has never been greater than it is today, and will only continue to grow at a rapid pace. Expert authors remove the veil of unnecessary complexity that often surrounds machine learning and deep learning by employing a narrative style that emphasizes intuition in place of abstract mathematical formalisms, allowing them to strike a delicate balance between practicality and theoretical rigor in service of facilitating the reader’s learning experience. Topics covered in the book include: mathematical encoding of medical data, linear regression and classification, nonlinear feature engineering, deep learning, convolutional and recurrent neural networks, and reinforcement learning. Each chapter ends with a collection of exercises for readers to practice and test their knowledge. This is an ideal introduction for medical students, professionals, and researchers interested in learning more about machine learning and deep learning. Readers who have taken at least one introductory mathematics course at the undergraduate-level (e.g., biostatistics or calculus) will be well-equipped to use this book without needing any additional prerequisites.

Download or read book TensorFlow Machine Learning Cookbook written by Nick McClure and published by Packt Publishing Ltd. This book was released on 2017-02-14 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore machine learning concepts using the latest numerical computing library — TensorFlow — with the help of this comprehensive cookbook About This Book Your quick guide to implementing TensorFlow in your day-to-day machine learning activities Learn advanced techniques that bring more accuracy and speed to machine learning Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow Who This Book Is For This book is ideal for data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. Intermediate and advanced machine learning implementers who need a quick guide they can easily navigate will find it useful. What You Will Learn Become familiar with the basics of the TensorFlow machine learning library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Take TensorFlow into production In Detail TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production. Style and approach This book takes a recipe-based approach where every topic is explicated with the help of a real-world example.

Download or read book Deep Learning Cookbook written by Douwe Osinga and published by "O'Reilly Media, Inc.". This book was released on 2018-06-05 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve deep-learning problems for classifying and generating text, images, and music. Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks. You’ll learn how to: Create applications that will serve real users Use word embeddings to calculate text similarity Build a movie recommender system based on Wikipedia links Learn how AIs see the world by visualizing their internal state Build a model to suggest emojis for pieces of text Reuse pretrained networks to build an inverse image search service Compare how GANs, autoencoders and LSTMs generate icons Detect music styles and index song collections

Download or read book Machine Learning with Python Cookbook written by Chris Albon and published by "O'Reilly Media, Inc.". This book was released on 2018-03-09 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models

Download or read book Python Machine Learning Cookbook written by Prateek Joshi and published by Packt Publishing Ltd. This book was released on 2016-06-23 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: 100 recipes that teach you how to perform various machine learning tasks in the real world About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide Learn about perceptrons and see how they are used to build neural networks Stuck while making sense of images, text, speech, and real estate? This guide will come to your rescue, showing you how to perform machine learning for each one of these using various techniques Who This Book Is For This book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code. What You Will Learn Explore classification algorithms and apply them to the income bracket estimation problem Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Explore data visualization techniques to interact with your data in diverse ways Find out how to build a recommendation engine Understand how to interact with text data and build models to analyze it Work with speech data and recognize spoken words using Hidden Markov Models Analyze stock market data using Conditional Random Fields Work with image data and build systems for image recognition and biometric face recognition Grasp how to use deep neural networks to build an optical character recognition system In Detail Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples. Style and approach You will explore various real-life scenarios in this book where machine learning can be used, and learn about different building blocks of machine learning using independent recipes in the book.

Download or read book Master Machine Learning Algorithms written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2016-03-04 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: You must understand the algorithms to get good (and be recognized as being good) at machine learning. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step.

Download or read book Machine Learning Using TensorFlow Cookbook written by Alexia Audevart and published by Packt Publishing Ltd. This book was released on 2021-02-08 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive recipes to give you valuable insights on Transformers, Reinforcement Learning, and more Key FeaturesDeep Learning solutions from Kaggle Masters and Google Developer ExpertsGet to grips with the fundamentals including variables, matrices, and data sourcesLearn advanced techniques to make your algorithms faster and more accurateBook Description The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios. What you will learnTake TensorFlow into productionImplement and fine-tune Transformer models for various NLP tasksApply reinforcement learning algorithms using the TF-Agents frameworkUnderstand linear regression techniques and use Estimators to train linear modelsExecute neural networks and improve predictions on tabular dataMaster convolutional neural networks and recurrent neural networks through practical recipesWho this book is for If you are a data scientist or a machine learning engineer, and you want to skip detailed theoretical explanations in favor of building production-ready machine learning models using TensorFlow, this book is for you. Basic familiarity with Python, linear algebra, statistics, and machine learning is necessary to make the most out of this book.

Download or read book Mastering the Art of Cookbook Medicine written by Jason Abaluck and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of machine learning (ML) to randomized controlled trials (RCTs) can quantify and improve misallocation in healthcare. We study the decision to prescribe anticoagulants for atrial fibrillation patients; anticoagulation reduces stroke risk but increases hemorrhage risk. We combine observational data on treatment choice and guideline use with ML estimates of heterogeneous treatment effects from eight RCTs. When physicians adopt a clinical guideline, treatment decisions shift towards the recommendation but adherence remains far from perfect. Improving guideline adherence would produce larger gains than informing physicians about guidelines. Adherence to an optimal rule would prevent 47% more strokes without increasing hemorrhages.

Download or read book Mastering the Art of Cookbook Medicine written by Jason T. Abaluck and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of machine learning (ML) to randomized controlled trials (RCTs) can quantify and improve misallocation in healthcare. We study the decision to prescribe anticoagulants for atrial fibrillation patients; anticoagulation reduces stroke risk but increases hemorrhage risk. We combine observational data on treatment choice and guideline use with ML estimates of heterogeneous treatment effects from eight RCTs. When physicians adopt a clinical guideline, treatment decisions shift towards the recommendation but adherence remains far from perfect. Improving guideline adherence would produce larger gains than informing physicians about guidelines. Adherence to an optimal rule would prevent 47% more strokes without increasing hemorrhages.