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Prediction of Cancer Patient Outcomes Based on Artificial Intelligence

2019 Computers
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Release : 2019
Genre : Computers
Kind : eBook
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Book Synopsis Prediction of Cancer Patient Outcomes Based on Artificial Intelligence by : Suk Lee

Download or read book Prediction of Cancer Patient Outcomes Based on Artificial Intelligence written by Suk Lee. This book was released on 2019. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial intelligence are described.

Prognostication and Prediction of Cancer Patient Outcomes Using AI-based Classifiers

2023
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Release : 2023
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Kind : eBook
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Book Synopsis Prognostication and Prediction of Cancer Patient Outcomes Using AI-based Classifiers by : Cristiano Guttà

Download or read book Prognostication and Prediction of Cancer Patient Outcomes Using AI-based Classifiers written by Cristiano Guttà. This book was released on 2023. Available in PDF, EPUB and Kindle. Book excerpt:

Cancer Prediction for Industrial IoT 4.0

2021-12-31 Computers
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Release : 2021-12-31
Genre : Computers
Kind : eBook
Book Rating : 668/5 ( reviews)

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Book Synopsis Cancer Prediction for Industrial IoT 4.0 by : Meenu Gupta

Download or read book Cancer Prediction for Industrial IoT 4.0 written by Meenu Gupta. This book was released on 2021-12-31. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Artificial Intelligence In Radiation Oncology

2022-12-27 Science
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Release : 2022-12-27
Genre : Science
Kind : eBook
Book Rating : 558/5 ( reviews)

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Book Synopsis Artificial Intelligence In Radiation Oncology by : Seong K Mun

Download or read book Artificial Intelligence In Radiation Oncology written by Seong K Mun. This book was released on 2022-12-27. Available in PDF, EPUB and Kindle. Book excerpt: The clinical use of Artificial Intelligence (AI) in radiation oncology is in its infancy. However, it is certain that AI is capable of making radiation oncology more precise and personalized with improved outcomes. Radiation oncology deploys an array of state-of-the-art technologies for imaging, treatment, planning, simulation, targeting, and quality assurance while managing the massive amount of data involving therapists, dosimetrists, physicists, nurses, technologists, and managers. AI consists of many powerful tools which can process a huge amount of inter-related data to improve accuracy, productivity, and automation in complex operations such as radiation oncology.This book offers an array of AI scientific concepts, and AI technology tools with selected examples of current applications to serve as a one-stop AI resource for the radiation oncology community. The clinical adoption, beyond research, will require ethical considerations and a framework for an overall assessment of AI as a set of powerful tools.30 renowned experts contributed to sixteen chapters organized into six sections: Define the Future, Strategy, AI Tools, AI Applications, and Assessment and Outcomes. The future is defined from a clinical and a technical perspective and the strategy discusses lessons learned from radiology experience in AI and the role of open access data to enhance the performance of AI tools. The AI tools include radiomics, segmentation, knowledge representation, and natural language processing. The AI applications discuss knowledge-based treatment planning and automation, AI-based treatment planning, prediction of radiotherapy toxicity, radiomics in cancer prognostication and treatment response, and the use of AI for mitigation of error propagation. The sixth section elucidates two critical issues in the clinical adoption: ethical issues and the evaluation of AI as a transformative technology.

Optimized Predictive Models in Health Care Using Machine Learning

2024-03-06 Computers
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Release : 2024-03-06
Genre : Computers
Kind : eBook
Book Rating : 624/5 ( reviews)

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Book Synopsis Optimized Predictive Models in Health Care Using Machine Learning by : Sandeep Kumar

Download or read book Optimized Predictive Models in Health Care Using Machine Learning written by Sandeep Kumar. This book was released on 2024-03-06. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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