Tracking and Preventing Diseases with Artificial Intelligence da Mehta, Mayuri (Edited by)/ Fournier-Viger, Philippe (Edited by)/ Patel, Maulika (Edited by)/ Lin, Jerry Chun-Wei (Edited by): Brand New Hardcover (2021)

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272 pages. 9.25x6.10x0.75 inches. In Stock.

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Dati bibliografici

Titolo: Tracking and Preventing Diseases with Artificial Intelligence
Editore: Springer
Data di pubblicazione: 2021
Lingua: Inglese
ISBN 10: 3030767310
ISBN 13: 9783030767310
Rilegatura: Hardcover
Condizione: Brand New

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Riassunto

This book presents an overview of how machine learning and data mining techniques are used for tracking and preventing diseases. It covers several aspects such as stress level identification of a person from his/her speech, automatic diagnosis of disease from X-ray images, intelligent diagnosis of Glaucoma from clinical eye examination data, prediction of protein-coding genes from big genome data, disease detection through microscopic analysis of blood cells, information retrieval from electronic medical record using named entity recognition approaches, and prediction of drug-target interactions.

The book is suitable for computer scientists having a bachelor degree in computer science. The book is an ideal resource as a reference book for teaching a graduate course on AI for Medicine or AI for Health care. Researchers working in the multidisciplinary areas use this book to discover the current developments. Besides itsuse in academia, this book provides enough details about the state-of-the-art algorithms addressing various biomedical domains, so that it could be used by industry practitioners who want to implement AI techniques to analyze the diseases. Medical institutions use this book as reference material and give tutorials to medical experts on how the advanced AI and ML techniques contribute to the diagnosis and prediction of the diseases.

Dalla quarta di copertina

This book presents an overview of how machine learning and data mining techniques are used for tracking and preventing diseases. It covers several aspects such as stress level identification of a person from his/her speech, automatic diagnosis of disease from X-ray images, intelligent diagnosis of Glaucoma from clinical eye examination data, prediction of protein-coding genes from big genome data, disease detection through microscopic analysis of blood cells, information retrieval from electronic medical record using named entity recognition approaches, and prediction of drug-target interactions.

The book is suitable for computer scientists having a bachelor degree in computer science. The book is an ideal resource as a reference book for teaching a graduate course on AI for Medicine or AI for Health care. Researchers working in the multidisciplinary areas use this book to discover the current developments. Besides itsuse in academia, this book provides enough details about the state-of-the-art algorithms addressing various biomedical domains, so that it could be used by industry practitioners who want to implement AI techniques to analyze the diseases. Medical institutions use this book as reference material and give tutorials to medical experts on how the advanced AI and ML techniques contribute to the diagnosis and prediction of the diseases.

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