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Book Synopsis Machine learning-based adaptive radiotherapy treatments: From bench top to bedside by : Jiahan Zhang
Download or read book Machine learning-based adaptive radiotherapy treatments: From bench top to bedside written by Jiahan Zhang and published by Frontiers Media SA. This book was released on 2023-05-12 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt:
This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Book Synopsis Machine Learning in Radiation Oncology by : Issam El Naqa
Download or read book Machine Learning in Radiation Oncology written by Issam El Naqa and published by Springer. This book was released on 2015-06-19 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.
Book Synopsis Artificial Intelligence in Medicine by : David Riaño
Download or read book Artificial Intelligence in Medicine written by David Riaño and published by Springer. This book was released on 2019-06-19 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.
Cancer is a complex disease process that spans multiple scales in space and time. Driven by cutting-edge mathematical and computational techniques, in silico biology provides powerful tools to investigate the mechanistic relationships of genes, cells, and tissues. It enables the creation of experimentally testable hypotheses, the integration of dat
Book Synopsis Multiscale Cancer Modeling by : Thomas S. Deisboeck
Download or read book Multiscale Cancer Modeling written by Thomas S. Deisboeck and published by CRC Press. This book was released on 2010-12-08 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cancer is a complex disease process that spans multiple scales in space and time. Driven by cutting-edge mathematical and computational techniques, in silico biology provides powerful tools to investigate the mechanistic relationships of genes, cells, and tissues. It enables the creation of experimentally testable hypotheses, the integration of dat
In this book we provide insights into liver – cancer and immunology. Experts in the field provide an overview over fundamental immunological questions in liver cancer and tumorimmunology, which form the base for immune based approaches in HCC, which gain increasing interest in the community due to first promising results obtained in early clinical trials. Hepatocellular carcinoma (HCC) is the third most common cause of cancer related death in the United States. Treatment options are limited. Viral hepatitis is one of the major risk factors for HCC, which represents a typical “inflammation-induced” cancer. Immune-based treatment approaches have revolutionized oncology in recent years. Various treatment strategies have received FDA approval including dendritic cell vaccination, for prostate cancer as well as immune checkpoint inhibition targeting the CTLA4 or the PD1/PDL1 axis in melanoma, lung, and kidney cancer. Additionally, cell based therapies (adoptive T cell therapy, CAR T cells and TCR transduced T cells) have demonstrated significant efficacy in patients with B cell malignancies and melanoma. Immune checkpoint inhibitors in particular have generated enormous excitement across the entire field of oncology, providing a significant benefit to a minority of patients.
Book Synopsis Immunotherapy of Hepatocellular Carcinoma by : Tim F. Greten
Download or read book Immunotherapy of Hepatocellular Carcinoma written by Tim F. Greten and published by Springer. This book was released on 2018-08-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book we provide insights into liver – cancer and immunology. Experts in the field provide an overview over fundamental immunological questions in liver cancer and tumorimmunology, which form the base for immune based approaches in HCC, which gain increasing interest in the community due to first promising results obtained in early clinical trials. Hepatocellular carcinoma (HCC) is the third most common cause of cancer related death in the United States. Treatment options are limited. Viral hepatitis is one of the major risk factors for HCC, which represents a typical “inflammation-induced” cancer. Immune-based treatment approaches have revolutionized oncology in recent years. Various treatment strategies have received FDA approval including dendritic cell vaccination, for prostate cancer as well as immune checkpoint inhibition targeting the CTLA4 or the PD1/PDL1 axis in melanoma, lung, and kidney cancer. Additionally, cell based therapies (adoptive T cell therapy, CAR T cells and TCR transduced T cells) have demonstrated significant efficacy in patients with B cell malignancies and melanoma. Immune checkpoint inhibitors in particular have generated enormous excitement across the entire field of oncology, providing a significant benefit to a minority of patients.
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.
Book Synopsis Machine Learning with Health Care Perspective by : Vishal Jain
Download or read book Machine Learning with Health Care Perspective written by Vishal Jain and published by Springer Nature. This book was released on 2020-03-09 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
This is the model list and clearing house of appropriate, basic, and priority medical devices based on the list of clinical interventions selected from clinical guidelines on prevention, screening, diagnosis, treatment, palliative care, monitoring, and end of life care. This publication addresses medical devices that can be used for the management of cancer and specifically describes medical devices for six types of cancer: breast, cervical, colorectal, leukemia, lung, and prostate. This book is intended for ministries of health, public health planners, health technology managers, disease management, researchers, policy makers, funding, and procurement agencies and support and advocacy groups for cancer patients.
Book Synopsis WHO List of Priority Medical Devices for Cancer Management by : World Health Organization
Download or read book WHO List of Priority Medical Devices for Cancer Management written by World Health Organization and published by . This book was released on 2017-05-09 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the model list and clearing house of appropriate, basic, and priority medical devices based on the list of clinical interventions selected from clinical guidelines on prevention, screening, diagnosis, treatment, palliative care, monitoring, and end of life care. This publication addresses medical devices that can be used for the management of cancer and specifically describes medical devices for six types of cancer: breast, cervical, colorectal, leukemia, lung, and prostate. This book is intended for ministries of health, public health planners, health technology managers, disease management, researchers, policy makers, funding, and procurement agencies and support and advocacy groups for cancer patients.
Clinical Case Studies for the Family Nurse Practitioner is a key resource for advanced practice nurses and graduate students seeking to test their skills in assessing, diagnosing, and managing cases in family and primary care. Composed of more than 70 cases ranging from common to unique, the book compiles years of experience from experts in the field. It is organized chronologically, presenting cases from neonatal to geriatric care in a standard approach built on the SOAP format. This includes differential diagnosis and a series of critical thinking questions ideal for self-assessment or classroom use.
Book Synopsis Clinical Case Studies for the Family Nurse Practitioner by : Leslie Neal-Boylan
Download or read book Clinical Case Studies for the Family Nurse Practitioner written by Leslie Neal-Boylan and published by John Wiley & Sons. This book was released on 2011-11-28 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clinical Case Studies for the Family Nurse Practitioner is a key resource for advanced practice nurses and graduate students seeking to test their skills in assessing, diagnosing, and managing cases in family and primary care. Composed of more than 70 cases ranging from common to unique, the book compiles years of experience from experts in the field. It is organized chronologically, presenting cases from neonatal to geriatric care in a standard approach built on the SOAP format. This includes differential diagnosis and a series of critical thinking questions ideal for self-assessment or classroom use.
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
Book Synopsis Fundamentals of Clinical Data Science by : Pieter Kubben
Download or read book Fundamentals of Clinical Data Science written by Pieter Kubben and published by Springer. This book was released on 2018-12-21 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
Book Synopsis Artificial Intelligence in Medical Imaging by : Erik R. Ranschaert
Download or read book Artificial Intelligence in Medical Imaging written by Erik R. Ranschaert and published by Springer. This book was released on 2019-01-29 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
