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03159nmm a2200361 u 4500 |
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EBX01000000000000001170227 |
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00000000000000.0 |
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| 008 |
220104 ||| eng |
| 020 |
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|a 9783030767327
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| 100 |
1 |
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|a Mehta, Mayuri
|e [editor]
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| 245 |
0 |
0 |
|a Tracking and Preventing Diseases with Artificial Intelligence
|h Elektronische Ressource
|c edited by Mayuri Mehta, Philippe Fournier-Viger, Maulika Patel, Jerry Chun-Wei Lin
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| 250 |
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|a 1st ed. 2022
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2022, 2022
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| 300 |
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|a XX, 252 p. 117 illus., 86 illus. in color
|b online resource
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| 505 |
0 |
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|a Stress Identification from Speech using Clustering techniques -- Comparative Study and Detection of COVID-19 and Related Viral Pneumonia using a Fine-tuned Deep Transfer Learning -- Predicting Glaucoma Diagnosis using AI -- Diagnosis and Analysis of Tuberculosis Disease using Simple Neural Network and Deep Learning Approach for Chest X-ray Images -- Adaptive Machine Learning Algorithm and Analytics of Big Genomic Data for Gene Prediction
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| 653 |
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|a Computational intelligence
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Biomedical engineering
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| 653 |
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|a Biomedical Engineering and Bioengineering
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Computational Intelligence
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| 700 |
1 |
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|a Fournier-Viger, Philippe
|e [editor]
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| 700 |
1 |
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|a Patel, Maulika
|e [editor]
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| 700 |
1 |
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|a Lin, Jerry Chun-Wei
|e [editor]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Intelligent Systems Reference Library
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| 028 |
5 |
0 |
|a 10.1007/978-3-030-76732-7
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-030-76732-7?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 006.3
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| 520 |
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|a 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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