Interpretable Machine Learning Methods With Applications To Health Care

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Interpretable Machine Learning Methods with Applications to Health Care

Author : Yuchen Wang
Publisher :
ISBN 13 :
Total Pages : 142 pages
Book Rating : 4.:/5 (119 download)

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Book Synopsis Interpretable Machine Learning Methods with Applications to Health Care by : Yuchen Wang

Download or read book Interpretable Machine Learning Methods with Applications to Health Care written by Yuchen Wang and published by . This book was released on 2020 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: With data becoming increasingly available in recent years, black-box algorithms like boosting methods or neural networks play more important roles in the real world. However, interpretability is a severe need for several areas of applications, like health care or business. Doctors or managers often need to understand how models make predictions, in order to make their final decisions. In this thesis, we improve and propose some interpretable machine learning methods by using modern optimization. We also use two examples to illustrate how interpretable machine learning methods help to solve problems in health care. The first part of this thesis is about interpretable machine learning methods using modern optimization. In Chapter 2, we illustrate how to use robust optimization to improve the performance of SVM, Logistic Regression, and Classification Trees for imbalanced datasets. In Chapter 3, we discuss how to find optimal clusters for prediction. we use real-world datasets to illustrate this is a fast and scalable method with high accuracy. In Chapter 4, we deal with optimal regression trees with polynomial function in leaf nodes and demonstrate this method improves the out-of-sample performance. The second part of this thesis is about how interpretable machine learning methods improve the current health care system. In Chapter 5, we illustrate how we use Optimal Trees to predict the risk mortality for candidates awaiting liver transplantation. Then we develop a transplantation policy called Optimized Prediction of Mortality (OPOM), which reduces mortality significantly in simulation analysis and also improves fairness. In Chapter 6, we propose a new method based on Optimal Trees which perform better than original rules in identifying children at very low risk of clinically important traumatic brain injury (ciTBI). If this method is implemented in the electronic health record, the new rules may reduce unnecessary computed tomographies (CT).

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Data-driven Modeling and Interpretable Machine Learning with Applications in Healthcare

Author : Ning Liu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (113 download)

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Book Synopsis Data-driven Modeling and Interpretable Machine Learning with Applications in Healthcare by : Ning Liu

Download or read book Data-driven Modeling and Interpretable Machine Learning with Applications in Healthcare written by Ning Liu and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The promise of machine learning in transforming all aspects of healthcare ecosystemshas received global attention. Machine learning employs sophisticated algorithms totransform massive amounts of data into actionable insights, and ambitiously leadsthe way in reshaping the healthcare industry. Owing to the unique characteristicsof healthcare data and the highly-regulated nature of the healthcare industry,challenges largely remain in successfully applying machine learning to healthcare.Data generated in healthcare usually comes from various sources across multipleservice units and agencies. Besides the issues of inconsistency and redundancy,healthcare data are generally noisy, sparse, unstructured, and heterogeneous. Thedata quality issues pose severe threats to the accuracy and authenticity of machinelearning results. Furthermore, healthcare decisions and policies derived frommachine learning models must be interpretable and can be intuitively understoodby health professionals. However, most of the best-performing machine learningmodels tend to function like a black box and fail to provide any explanations onhow the decisions are reached; the lack of transparency creates barriers for humansto understand and trust model results. As with any other high-stakes decisionsituations, understanding the reasons why the model works is as important as whatthe prediction result is. The surge of interests in model interpretability has led tothe development of interpretable machine learning techniques.In response to the data quality and model interpretability challenges, thisdissertation explores three essential and interrelated healthcare analytics problemswith viewpoints from data-driven modeling and interpretable machine learning.In the first problem, we investigate utilizing a set of health-related databases toidentify high-priority drug-drug iterations (DDIs) for use in medication alerts. Wepropose a data-driven framework to extract useful features from the FDA adverseevent reports and develop an autoencoder-based semi-supervised learning algorithmto make inferences about potential high-priority DDIs. The experimental resultsdemonstrate the effectiveness of using adverse event feature representations indifferentiating high- and low-priority DDIs. Moreover, the proposed algorithmutilizes stacked autoencoders and unlabeled samples for boosting classificationperformance, which outperforms other competing semi-supervised methods. Thesecond and third problems are related to patient satisfaction studies. We focuson decoding the mysteries behind patient satisfaction using the insights extractedfrom hospital electronic health records and patient survey data. In the secondproblem, we propose an interpretable machine learning framework that transformsheterogeneous data into human-understandable feature representations and thenutilizes a mixed-integer programming model to discover the major factors thatinfluence patient satisfaction. In the third problem, we introduce a post hoc localexplanation method to interpret black-box model outputs aiming at closing the gapbetween model decisions and the understanding of healthcare users. Results of thereal-world case studies show that factors related to the courtesy and respect fromnurses and doctors, communication between health professionals and patients, andhospital discharge instructions significantly impact the overall patient satisfaction.Our approach and findings help establish guidelines for quality healthcare in thefuture.

Machine Learning for Healthcare Applications

Author : Sachi Nandan Mohanty
Publisher : John Wiley & Sons
ISBN 13 : 1119791812
Total Pages : 418 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Machine Learning for Healthcare Applications by : Sachi Nandan Mohanty

Download or read book Machine Learning for Healthcare Applications written by Sachi Nandan Mohanty and published by John Wiley & Sons. This book was released on 2021-04-13 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment. Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning. This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.

Explainable Machine Learning for Multimedia Based Healthcare Applications

Author : M. Shamim Hossain
Publisher : Springer Nature
ISBN 13 : 3031380363
Total Pages : 240 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Explainable Machine Learning for Multimedia Based Healthcare Applications by : M. Shamim Hossain

Download or read book Explainable Machine Learning for Multimedia Based Healthcare Applications written by M. Shamim Hossain and published by Springer Nature. This book was released on with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Interpretable Machine Learning Methods for Stroke Prediction

Author : Rebecca Zhang (S.M.)
Publisher :
ISBN 13 :
Total Pages : 75 pages
Book Rating : 4.:/5 (113 download)

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Book Synopsis Interpretable Machine Learning Methods for Stroke Prediction by : Rebecca Zhang (S.M.)

Download or read book Interpretable Machine Learning Methods for Stroke Prediction written by Rebecca Zhang (S.M.) and published by . This book was released on 2019 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has long been touted as the next big tool, revolutionizing scientific endeavors as well as impacting industries like retail and finance. Naturally, there is much interest in the potential of next improving healthcare. However, using traditional machine learning approaches in this domain has many difficulties, chief among which is the issue of interpretability. We focus on the medical condition of stroke, a particularly desirable problem to target because it is one of the most prevalent and yet preventable conditions affecting Americans today. In this thesis, we apply novel interpretable prediction techniques to the problem of predicting stroke presence, location, acuity, and mortality risk for patient populations at two different hospital systems. We show that using an interpretable, optimal tree-based approach is roughly as effective if not better than black-box approaches. Using the clinical learnings from these studies, we explore new interpretable methodologies designed with medical applications and their unique challenges in mind. We present a novel regression algorithm to predict outcomes when the population is comprised of notably different subpopulations, and demonstrate that this gives comparable performance with improved interpretability. Finally, we explore new natural language processing techniques for machine learning from text. We propose an alternate end-to- end framework for going from unprocessed textual data to predictions, with an interpretable linguistics-based approach to model words. Altogether, this work demonstrates the promise that new parsimonious, interpretable algorithms have in the domain of stroke and broader healthcare problems.

Introduction to Deep Learning for Healthcare

Author : Cao Xiao
Publisher : Springer Nature
ISBN 13 : 3030821846
Total Pages : 236 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Introduction to Deep Learning for Healthcare by : Cao Xiao

Download or read book Introduction to Deep Learning for Healthcare written by Cao Xiao and published by Springer Nature. This book was released on 2021-11-11 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents deep learning models and their healthcare applications. It focuses on rich health data and deep learning models that can effectively model health data. Healthcare data: Among all healthcare technologies, electronic health records (EHRs) had vast adoption and a significant impact on healthcare delivery in recent years. One crucial benefit of EHRs is to capture all the patient encounters with rich multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for diagnoses and procedures, lab results, and medication information. Unstructured data contain 1) clinical notes as text, 2) medical imaging data such as X-rays, echocardiogram, and magnetic resonance imaging (MRI), and 3) time-series data such as the electrocardiogram (ECG) and electroencephalogram (EEG). Beyond the data collected during clinical visits, patient self-generated/reported data start to grow thanks to wearable sensors’ increasing use. The authors present deep learning case studies on all data described. Deep learning models: Neural network models are a class of machine learning methods with a long history. Deep learning models are neural networks of many layers, which can extract multiple levels of features from raw data. Deep learning applied to healthcare is a natural and promising direction with many initial successes. The authors cover deep neural networks, convolutional neural networks, recurrent neural networks, embedding methods, autoencoders, attention models, graph neural networks, memory networks, and generative models. It’s presented with concrete healthcare case studies such as clinical predictive modeling, readmission prediction, phenotyping, x-ray classification, ECG diagnosis, sleep monitoring, automatic diagnosis coding from clinical notes, automatic deidentification, medication recommendation, drug discovery (drug property prediction and molecule generation), and clinical trial matching. This textbook targets graduate-level students focused on deep learning methods and their healthcare applications. It can be used for the concepts of deep learning and its applications as well. Researchers working in this field will also find this book to be extremely useful and valuable for their research.

Machine Learning in Healthcare

Author : Bikesh Kumar Singh
Publisher : CRC Press
ISBN 13 : 1000540405
Total Pages : 248 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Machine Learning in Healthcare by : Bikesh Kumar Singh

Download or read book Machine Learning in Healthcare written by Bikesh Kumar Singh and published by CRC Press. This book was released on 2022-02-17 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) and machine learning (ML) techniques play an important role in our daily lives by enhancing predictions and decision-making for the public in several fields such as financial services, real estate business, consumer goods, social media, etc. Despite several studies that have proved the efficacy of AI/ML tools in providing improved healthcare solutions, it has not gained the trust of health-care practitioners and medical scientists. This is due to poor reporting of the technology, variability in medical data, small datasets, and lack of standard guidelines for application of AI. Therefore, the development of new AI/ML tools for various domains of medicine is an ongoing field of research. Machine Learning in Healthcare: Fundamentals and Recent Applications discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis, early detection and diagnosis of disease, providing objective-based evidence to reduce human errors, curtailing inter- and intra-observer errors, risk identification and interventions for healthcare management, real-time health monitoring, assisting clinicians and patients for selecting appropriate medications, and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided, along with solved examples and exercises. This text is ideal for readers interested in machine learning without any background knowledge and looking to implement machine-learning models for healthcare systems.

Interpretable Machine Learning with Python

Author : Serg Masís
Publisher : Packt Publishing Ltd
ISBN 13 : 1800206577
Total Pages : 737 pages
Book Rating : 4.8/5 (2 download)

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Book Synopsis Interpretable Machine Learning with Python by : Serg Masís

Download or read book Interpretable Machine Learning with Python written by Serg Masís and published by Packt Publishing Ltd. This book was released on 2021-03-26 with total page 737 pages. Available in PDF, EPUB and Kindle. Book excerpt: A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learn Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is for This book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along.

Artificial Intelligence and Machine Learning in Health Care and Medical Sciences

Author : Gyorgy J. Simon
Publisher : Springer Nature
ISBN 13 : 3031393554
Total Pages : 824 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Artificial Intelligence and Machine Learning in Health Care and Medical Sciences by : Gyorgy J. Simon

Download or read book Artificial Intelligence and Machine Learning in Health Care and Medical Sciences written by Gyorgy J. Simon and published by Springer Nature. This book was released on with total page 824 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Machine Learning Under a Modern Optimization Lens

Author : Dimitris Bertsimas
Publisher :
ISBN 13 : 9781733788502
Total Pages : 589 pages
Book Rating : 4.7/5 (885 download)

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Book Synopsis Machine Learning Under a Modern Optimization Lens by : Dimitris Bertsimas

Download or read book Machine Learning Under a Modern Optimization Lens written by Dimitris Bertsimas and published by . This book was released on 2019 with total page 589 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems

Author : Om Prakash Jena
Publisher : CRC Press
ISBN 13 : 1000486796
Total Pages : 397 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems by : Om Prakash Jena

Download or read book Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems written by Om Prakash Jena and published by CRC Press. This book was released on 2022-05-18 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of medical informatics is to improve life expectancy, disease diagnosis and quality of life. Medical devices have revolutionized healthcare and have led to the modern age of machine learning, deep learning and Internet of Medical Things (IoMT) with their proliferation, mobility and agility. This book exposes different dimensions of applications for computational intelligence and explains its use in solving various biomedical and healthcare problems in the real world. This book describes the fundamental concepts of machine learning and deep learning techniques in a healthcare system. The aim of this book is to describe how deep learning methods are used to ensure high-quality data processing, medical image and signal analysis and improved healthcare applications. This book also explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems. Furthermore, it provides the healthcare sector with innovative advances in theory, analytical approaches, numerical simulation, statistical analysis, modelling, advanced deployment, case studies, analytical results, computational structuring and significant progress in the field of machine learning and deep learning in healthcare applications. FEATURES Explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems Provides guidance in developing intelligence-based diagnostic systems, efficient models and cost-effective machines Provides the latest research findings, solutions to the concerning issues and relevant theoretical frameworks in the area of machine learning and deep learning for healthcare systems Describes experiences and findings relating to protocol design, prototyping, experimental evaluation, real testbeds and empirical characterization of security and privacy interoperability issues in healthcare applications Explores and illustrates the current and future impacts of pandemics and mitigates risk in healthcare with advanced analytics This book is intended for students, researchers, professionals and policy makers working in the fields of public health and in the healthcare sector. Scientists and IT specialists will also find this book beneficial for research exposure and new ideas in the field of machine learning and deep learning.

Interpretable Cognitive Internet of Things for Healthcare

Author : Utku Kose
Publisher : Springer Nature
ISBN 13 : 3031086376
Total Pages : 203 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Interpretable Cognitive Internet of Things for Healthcare by : Utku Kose

Download or read book Interpretable Cognitive Internet of Things for Healthcare written by Utku Kose and published by Springer Nature. This book was released on 2023-06-26 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents research on how interpretable cognitive IoT can work to help with the massive amount of data in the healthcare industry. The authors give importance to IoT systems with intense machine learning features; this ensures the scope corresponds to use of cognitive IoT for understanding, reasoning, and learning from medical data. The authors discuss the interpretability of an intelligent system and its trustworthiness as a smart tool in the context of massive healthcare applications. As a whole, book combines three important topics: massive data, cognitive IoT, and interpretability. Topics include health data analytics for cognitive IoT, usability evaluation of cognitive IoT for healthcare, interpretable cognitive IoT for health robotics, and wearables in the context of IoT for healthcare. The book acts as a useful reference work for a wide audience including academicians, scientists, students, and professionals.

Machine Learning Algorithms and Applications in Health Care

Author : Matthew Sobiesk
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

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Book Synopsis Machine Learning Algorithms and Applications in Health Care by : Matthew Sobiesk

Download or read book Machine Learning Algorithms and Applications in Health Care written by Matthew Sobiesk and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: There have been many recent advances in machine learning, resulting in models which have had major impact in a variety of disciplines. Some of the best performing models are black boxes, which are not directly interpretable by humans. However, in some applications such as health care it is vital to use interpretable models to understand why the model is making its predictions, to ensure that using them to inform decision making will not unexpectedly harm the people it should instead be helping. This leads to the question of whether a trade off between predictive accuracy and interpretability exists, and how we can improve interpretable models' performances to reduce such trade offs if they do. In the first chapter, we show that optimal decision trees are equivalent in terms of modeling power to neural networks. Specifically, given a neural network (feedforward, convolutional, or recurrent), we construct a decision tree with hyperplane splits that has identical in-sample performance. Building on previous research showing that given a decision tree, we can construct a feedforward neural network with the same in-sample performance, we prove the two methods are equivalent. We further compare decision trees and neural networks empirically on data from [31] and find that they have comparable performance. In the second chapter, we propose a new machine learning method called Optimal Predictive Clustering (OPC). The method uses optimization with strong warm starts to simultaneously cluster data points and learn cluster-specific logistic regression models. It is designed to combine strong predictive performance, scalability, and interpretability. We then empirically compare OPC to a wide variety of other methods such as Optimal Regression Trees with Linear Predictors (ORT-L) and XGBoost. We find that our method performs on par with cutting edge interpretable methods, and that it enhances an ensemble of methods to achieve the best out-of-sample performance across all models. In the third chapter, we predict one year transplant outcomes for lung, liver, and kidney data to investigate whether predicted post-transplant outcomes should be included in the organ allocation system of organs other than lungs. We find that the models do not differentiate one-year graft survival or failure outcomes effectively enough to be useful components of the organ allocation process. We then theorize about possible reasons for this failure, including the actual transplant procedure having a large effect on the one-year graft outcome or the potential need for additional data, like genetic information.

Data Science for Healthcare

Author : Sergio Consoli
Publisher : Springer
ISBN 13 : 3030052494
Total Pages : 367 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Data Science for Healthcare by : Sergio Consoli

Download or read book Data Science for Healthcare written by Sergio Consoli and published by Springer. This book was released on 2019-02-23 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book.

Explainable AI in Healthcare

Author : Mehul S Raval
Publisher : CRC Press
ISBN 13 : 100090640X
Total Pages : 346 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Explainable AI in Healthcare by : Mehul S Raval

Download or read book Explainable AI in Healthcare written by Mehul S Raval and published by CRC Press. This book was released on 2023-07-17 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book combines technology and the medical domain. It covers advances in computer vision (CV) and machine learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in computer science, electronics and communications, information technology, instrumentation and control, and electrical engineering. This book will benefit readers in the following ways: Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health care Investigates bridges between computer scientists and physicians being built with XAI Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent Initiates discussions on human-AI relationships in health care Unites learning for privacy preservation in health care

Machine Learning for Healthcare

Author : Rashmi Agrawal
Publisher : CRC Press
ISBN 13 : 1000221881
Total Pages : 160 pages
Book Rating : 4.0/5 (2 download)

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Book Synopsis Machine Learning for Healthcare by : Rashmi Agrawal

Download or read book Machine Learning for Healthcare written by Rashmi Agrawal and published by CRC Press. This book was released on 2020-12-08 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector. The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.