Machine Learning For Healthcare
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Machine Learning In Healthcare
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Author : Bikesh Kumar Singh
language : en
Publisher: CRC Press
Release Date : 2022-02-17
Machine Learning In Healthcare written by Bikesh Kumar Singh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-17 with Computers categories.
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.
Introduction To Deep Learning For Healthcare
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Author : Cao Xiao
language : en
Publisher: Springer Nature
Release Date : 2021-11-11
Introduction To Deep Learning For Healthcare written by Cao Xiao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-11 with Medical categories.
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.
Artificial Intelligence And Machine Learning In Healthcare
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Author : Ankur Saxena
language : en
Publisher: Springer Nature
Release Date : 2021-05-06
Artificial Intelligence And Machine Learning In Healthcare written by Ankur Saxena and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-06 with Science categories.
This book reviews the application of artificial intelligence and machine learning in healthcare. It discusses integrating the principles of computer science, life science, and statistics incorporated into statistical models using existing data, discovering patterns in data to extract the information, and predicting the changes and diseases based on this data and models. The initial chapters of the book cover the practical applications of artificial intelligence for disease prognosis & management. Further, the role of artificial intelligence and machine learning is discussed with reference to specific diseases like diabetes mellitus, cancer, mycobacterium tuberculosis, and Covid-19. The chapters provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and artificial intelligence. The book also touches upon precision medicine, personalized medicine, and transfer learning, with the real examples. Further, it also discusses the use of machine learning and artificial intelligence for visualization, prediction, detection, and diagnosis of Covid -19. This book is a valuable source of information for programmers, healthcare professionals, and researchers interested in understanding the applications of artificial intelligence and machine learning in healthcare.
Machine Learning And The Internet Of Medical Things In Healthcare
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Author : Krishna Kant Singh
language : en
Publisher: Academic Press
Release Date : 2021-04-14
Machine Learning And The Internet Of Medical Things In Healthcare written by Krishna Kant Singh and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-14 with Science categories.
Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide. The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks. - Provides an introduction to the Internet of Medical Things through the principles and applications of machine learning - Explains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronics - Includes coverage of the evolution of healthcare applications with machine learning, including Clinical Decision Support Systems, artificial intelligence in biomedical engineering, and AI-enabled connected health informatics, supported by real-world case studies
Artificial Intelligence And Machine Learning In Healthcare
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Author : Arman Kilic
language : en
Publisher: Academic Press
Release Date : 2025-10-01
Artificial Intelligence And Machine Learning In Healthcare written by Arman Kilic and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-01 with Medical categories.
Artificial Intelligence and Machine Learning in Healthcare discusses the potential of groundbreaking technologies on the delivery of care. A lot have been said about how artificial intelligence and machine learning can improve healthcare, however there are still many doubts and concerns among health professionals, all of which are addressed in this book. Sections cover History and Basic Overview of AI and ML, with differentiation of supervised, unsupervised and deep learning, Applications of AI and ML in Healthcare, The Future of Healthcare with AI, Challenges to Adopting AI in Healthcare, and ethics and legal processes for implementation.This book is a valuable resource for bioinformaticians, clinicians, graduate students and several members of biomedical field who needs to get up to speed on the revolutionary role of AI and Machine Learning in healthcare. - Provides an overview of AI and ML to the medical practitioner who may not be well versed in these fields - Encompasses a thorough review of what has been accomplished and demonstrated recently in the fields of AI and ML in healthcare - Discusses the future of AI and ML in healthcare, with a review of possible wearable technology and software and how they may be used for medical care
Machine Learning For Health Informatics
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Author : Andreas Holzinger
language : en
Publisher: Springer
Release Date : 2016-12-09
Machine Learning For Health Informatics written by Andreas Holzinger and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-09 with Computers categories.
Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.
Machine Learning In Healthcare Informatics
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Author : Sumeet Dua
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-12-09
Machine Learning In Healthcare Informatics written by Sumeet Dua and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-09 with Technology & Engineering categories.
The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.
Data Analytics In Bioinformatics
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Author : Rabinarayan Satpathy
language : en
Publisher: John Wiley & Sons
Release Date : 2021-01-20
Data Analytics In Bioinformatics written by Rabinarayan Satpathy and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-20 with Computers categories.
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
Machine Learning For Healthcare Technologies
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Author : David A. Clifton
language : en
Publisher: IET
Release Date : 2016-10-28
Machine Learning For Healthcare Technologies written by David A. Clifton and has been published by IET this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-28 with Computers categories.
This book provides a snapshot of the state of current research at the interface between machine learning and healthcare with special emphasis on machine learning projects that are (or are close to) achieving improvement in patient outcomes. The book provides overviews on a range of technologies including detecting artefactual events in vital signs monitoring data; patient physiological monitoring; tracking infectious disease; predicting antibiotic resistance from genomic data; and managing chronic disease.
Machine Learning And Ai For Healthcare
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Author : Arjun Panesar
language : en
Publisher: Apress
Release Date : 2019-02-04
Machine Learning And Ai For Healthcare written by Arjun Panesar and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-04 with Computers categories.
Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll Learn Gain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agents Who This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.