› › Machine Learning Approaches To Prognostication In Supportive Care In Cancer

Machine Learning Approaches To Prognostication In Supportive Care In Cancer

Author : Andrew Davies

Publisher :

Page : pages

File Size : 38,62 MB

Release : 2017

Category :

ISBN :

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Book Description

MACHINE LEARNING APPROACHES TO PROGNOSTICATION IN SUPPORTIVE CARE IN CANCERIntroductionSurvival prediction is an important aspect of supportive care, and especially palliative care. However, robust survival prediction remains elusive. Machine learning approaches offer the potential to identify novel prognostic indicators, and so to develop more robust prognostic algorithms. Objectives The objective of this feasibility study was to develop a prognostic algorithm using machine learning for testing in a definitive study.Methods 50 patients with advanced cancer and an estimated prognosis of



Advanced Machine Learning Approaches in Cancer Prognosis

Author : Janmenjoy Nayak

Publisher : Springer Nature

Page : 461 pages

File Size : 29,24 MB

Release : 2021-05-29

Category : Technology & Engineering

ISBN : 3030719758

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Book Description

This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.



Cancer Prediction for Industrial IoT 4.0

Author : Meenu Gupta

Publisher : CRC Press

Page : 202 pages

File Size : 34,19 MB

Release : 2021-12-31

Category : Computers

ISBN : 1000508668

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Book Description

Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective explores various cancers using Artificial Intelligence techniques. It presents the rapid advancement in the existing prediction models by applying Machine Learning techniques. Several applications of Machine Learning in different cancer prediction and treatment options are discussed, including specific ideas, tools and practices most applicable to product/service development and innovation opportunities. The wide variety of topics covered offers readers multiple perspectives on various disciplines. Features • Covers the fundamentals, history, reality and challenges of cancer • Presents concepts and analysis of different cancers in humans • Discusses Machine Learning-based deep learning and data mining concepts in the prediction of cancer • Offers real-world examples of cancer prediction • Reviews strategies and tools used in cancer prediction • Explores the future prospects in cancer prediction and treatment Readers will learn the fundamental concepts and analysis of cancer prediction and treatment, including how to apply emerging technologies such as Machine Learning into practice to tackle challenges in domains/fields of cancer with real-world scenarios. Hands-on chapters contributed by academicians and other professionals from reputed organizations provide and describe frameworks, applications, best practices and case studies on emerging cancer treatment and predictions. This book will be a vital resource to graduate students, data scientists, Machine Learning researchers, medical professionals and analytics managers.



Machine and Deep Learning in Oncology, Medical Physics and Radiology

Author : Issam El Naqa

Publisher : Springer Nature

Page : 514 pages

File Size : 42,31 MB

Release : 2022-02-02

Category : Science

ISBN : 3030830470

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Book Description

This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.



Machine Learning and Artificial Intelligence in Radiation Oncology

Author : Barry S. Rosenstein

Publisher : Academic Press

Page : 480 pages

File Size : 44,36 MB

Release : 2023-12-02

Category : Science

ISBN : 0128220015

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Book Description

Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. It addresses the existing void in a resource to educate practicing clinicians about how machine learning can be used to improve clinical and patient-centered outcomes. This book is divided into three sections: the first addresses fundamental concepts of machine learning and radiation oncology, detailing techniques applied in genomics; the second section discusses translational opportunities, such as in radiogenomics and autosegmentation; and the final section encompasses current clinical applications in clinical decision making, how to integrate AI into workflow, use cases, and cross-collaborations with industry. The book is a valuable resource for oncologists, radiologists and several members of biomedical field who need to learn more about machine learning as a support for radiation oncology. Presents content written by practicing clinicians and research scientists, allowing a healthy mix of both new clinical ideas as well as perspectives on how to translate research findings into the clinic Provides perspectives from artificial intelligence (AI) industry researchers to discuss novel theoretical approaches and possibilities on academic collaborations Brings diverse points-of-view from an international group of experts to provide more balanced viewpoints on a complex topic



Multimodal Scene Understanding

Author : Michael Yang

Publisher : Academic Press

Page : 422 pages

File Size : 48,77 MB

Release : 2019-07-16

Category : Computers

ISBN : 0128173599

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Book Description

Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. Contains state-of-the-art developments on multi-modal computing Shines a focus on algorithms and applications Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning



Artificial Intelligence and Machine Learning in Healthcare

Author : Ankur Saxena

Publisher : Springer Nature

Page : 228 pages

File Size : 35,4 MB

Release : 2021-05-06

Category : Science

ISBN : 9811608113

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Book Description

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.



A Machine Learning Approach For Lung And Bronchus Cancer Survival Prediction

Author : Rouzbeh Talebizarinkamar

Publisher :

Page : 0 pages

File Size : 31,98 MB

Release : 2020

Category :

ISBN :

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Book Description

In 2019 National Cancer Institute (NCI) in the USA ranked lung and bronchus cancer as the second diagnosis of cancer types. It is important to mention that only a few studies have focused on lung and bronchus cancer patient's survival time by using the SEER database via Machine Learning techniques. This Thesis intends to develop a Machine Learning Approach to classify survivability (dead or survived), and in addition to classification, aims to predict the remaining lifespan for the patients who predicted would die within five years. In the first step, nine Machine Learning techniques, including Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes Classifier, Ensemble Max Voting, Stacking Ensemble, Random Forest, Gradient Boosting Machine, Adaboost, along with a proposed Deep Neural Network are applied to predict whether the patients would die or survive after five years. In the next step, we use another Deep Neural Network for regression for the patients who are predicted to die for actual survival prediction. The results show that the proposed Deep Neural Network outperformed other Machine learning techniques.



Machine Learning in Radiation Oncology

Author : Issam El Naqa

Publisher : Springer

Page : 336 pages

File Size : 43,15 MB

Release : 2015-06-19

Category : Medical

ISBN : 3319183052

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Book Description

​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.



Advanced Prognostic Predictive Modelling in Healthcare Data Analytics

Author : Sudipta Roy

Publisher : Springer Nature

Page : 317 pages

File Size : 33,8 MB

Release : 2021-04-22

Category : Technology & Engineering

ISBN : 9811605386

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Book Description

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.