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Edité par Taylor & Francis Ltd, London, 2024
ISBN 10 : 0367564432 ISBN 13 : 9780367564438
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Ajouter au panierPaperback. Etat : new. Paperback. 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. Machine Learning in Healthcare discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. It covers fundamental concepts including mathematical requisites and traditional machine-learning framework followed by advanced machine-learning methods and their applications in medical fields. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Edité par Taylor & Francis Ltd, London, 2024
ISBN 10 : 0367564432 ISBN 13 : 9780367564438
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Ajouter au panierPaperback. Etat : new. Paperback. 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. Machine Learning in Healthcare discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. It covers fundamental concepts including mathematical requisites and traditional machine-learning framework followed by advanced machine-learning methods and their applications in medical fields. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Edité par Springer International Publishing AG, Cham, 2024
ISBN 10 : 303154546X ISBN 13 : 9783031545467
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Ajouter au panierPaperback. Etat : new. Paperback. This CCIS post conference volume constitutes the proceedings of the Second International Conference, ICBEST 2023, in Raipur, India, in February 2023. The 32 full papers together in this volume were carefully reviewed and selected from 60 submissions. The conference's fundamental theme was "Computing in Biomedical Research". They were organized in three tracks as follows: Artificial Intelligence in Healthcare; Computational Mechanics in Healthcare and Health Informatics. This CCIS post conference volume constitutes the proceedings of the Second International Conference, ICBEST 2023, in Raipur, India, in February 2023. The 32 full papers together in this volume were carefully reviewed and selected from 60 submissions. The conference's fundamental theme was "Computing in Biomedical Research". Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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ISBN 10 : 0367564432 ISBN 13 : 9780367564438
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Ajouter au panierPaperback. Etat : new. Paperback. 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. Machine Learning in Healthcare discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. It covers fundamental concepts including mathematical requisites and traditional machine-learning framework followed by advanced machine-learning methods and their applications in medical fields. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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