Optimized Predictive Models In Health Care Using Machine Learning
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Optimized Predictive Models In Health Care Using Machine Learning
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Author : Sandeep Kumar
language : en
Publisher: John Wiley & Sons
Release Date : 2024-02-08
Optimized Predictive Models In Health Care Using Machine Learning written by Sandeep Kumar 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 2024-02-08 with Computers categories.
OPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application; emphasizes validating and evaluating predictive models; provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; discusses the challenges and limitations of predictive modeling in healthcare; highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.
Smart Predictive Healthcare Using Machine Learning Techniques
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Author : Dinesh Kumar
language : en
Publisher:
Release Date : 2023-07-03
Smart Predictive Healthcare Using Machine Learning Techniques written by Dinesh Kumar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-03 with categories.
Leveraging Data Science For Global Health
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Author : Leo Anthony Celi
language : en
Publisher: Springer Nature
Release Date : 2020-07-31
Leveraging Data Science For Global Health written by Leo Anthony Celi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-31 with Medical categories.
This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.
Machine Learning With Health Care Perspective
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Author : Vishal Jain
language : en
Publisher: Springer Nature
Release Date : 2020-03-09
Machine Learning With Health Care Perspective written by Vishal Jain and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-09 with Technology & Engineering categories.
This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.
Healthcare Big Data Analytics
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Author : Akash Kumar Bhoi
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-03-18
Healthcare Big Data Analytics written by Akash Kumar Bhoi and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-18 with Computers categories.
This book highlights how optimized big data applications can be used for patient monitoring and clinical diagnosis. In fact, IoT-based applications are data-driven and mostly employ modern optimization techniques. The book also explores challenges, opportunities, and future research directions, discussing the stages of data collection and pre-processing, as well as the associated challenges and issues in data handling and setup.
Advanced Prognostic Predictive Modelling In Healthcare Data Analytics
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Author : Sudipta Roy
language : en
Publisher: Springer Nature
Release Date : 2021-04-22
Advanced Prognostic Predictive Modelling In Healthcare Data Analytics written by Sudipta Roy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-22 with Technology & Engineering categories.
This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis. The use of prognostic modelling as predictive models to solve complex problems of data mining and analysis in health care is the feature of this book. The book examines the recent technologies and studies that reached the practical level and becoming available in preclinical and clinical practices in computational intelligence. The main areas of interest covered in this book are highest quality, original work that contributes to the basic science of processing, analysing and utilizing all aspects of advanced computational prognostic modelling in healthcare image and data analysis.
Artificial Intelligence And Machine Learning In Health Care And Medical Sciences
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Author : Gyorgy J. Simon
language : en
Publisher: Springer Nature
Release Date :
Artificial Intelligence And Machine Learning In Health Care And Medical Sciences written by Gyorgy J. Simon and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Artificial Intelligence And Data Mining In Healthcare
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Author : Malek Masmoudi
language : en
Publisher: Springer Nature
Release Date : 2021-01-25
Artificial Intelligence And Data Mining In Healthcare written by Malek Masmoudi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-25 with Computers categories.
This book presents recent work on healthcare management and engineering using artificial intelligence and data mining techniques. Specific topics covered in the contributed chapters include predictive mining, decision support, capacity management, patient flow optimization, image compression, data clustering, and feature selection. The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.
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.
Machine Learning For Healthcare
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Author : Rashmi Agrawal
language : en
Publisher: CRC Press
Release Date : 2020-12-08
Machine Learning For Healthcare written by Rashmi Agrawal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-08 with Computers categories.
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.