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Titlebook: Artificial Intelligence and Machine Learning in Healthcare; Ankur Saxena,Shivani Chandra Book 2021 The Editor(s) (if applicable) and The A

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發(fā)表于 2025-3-21 19:19:42 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Artificial Intelligence and Machine Learning in Healthcare
影響因子2023Ankur Saxena,Shivani Chandra
視頻videohttp://file.papertrans.cn/163/162243/162243.mp4
發(fā)行地址Analyzes the applications of artificial intelligence and machine learning in healthcare.Discusses the role of artificial intelligence in disease prognosis and management.Explores the role of AI in per
圖書封面Titlebook: Artificial Intelligence and Machine Learning in Healthcare;  Ankur Saxena,Shivani Chandra Book 2021 The Editor(s) (if applicable) and The A
影響因子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 pr
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發(fā)表于 2025-3-22 00:18:32 | 只看該作者
Artificial Intelligence in Personalized Medicine,garded in practicing an effective personalized medicine. Though machine learning is in the scenario of precision medicine first followed by personalized medicine, it still has to be accepted in the society for better development. This chapter gives an insight into how and where the artificial intelligence is used in the personalized medicine.
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發(fā)表于 2025-3-22 01:55:29 | 只看該作者
Book 2021science, 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 artific
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發(fā)表于 2025-3-22 05:19:26 | 只看該作者
https://doi.org/10.1007/978-3-540-75276-9rous missing values, errors, and biased inputs. This is due to under- or over-representation of certain groups of data, trivial data curation methods, etc. In this chapter, we describe Perceptive Bias, Processing Bias, and the ways to compute bias for Medical Big Data analysis.
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發(fā)表于 2025-3-22 12:26:45 | 只看該作者
Standards and Protocols for Interoperability map simulation of COVID-19’s international expansion to date and estimate virus distribution throughout all regions and countries. Detailed overview of both region-wise and state-wise recorded events; forecast of a viral pandemic attack, deaths, and recovered cases; and the degree to which it is spreading globally are included in this chapter.
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發(fā)表于 2025-3-22 14:54:31 | 只看該作者
Visualization and Prediction of COVID-19 Using AI and ML, map simulation of COVID-19’s international expansion to date and estimate virus distribution throughout all regions and countries. Detailed overview of both region-wise and state-wise recorded events; forecast of a viral pandemic attack, deaths, and recovered cases; and the degree to which it is spreading globally are included in this chapter.
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發(fā)表于 2025-3-23 00:59:28 | 只看該作者
Bias in Medical Big Data and Machine Learning Algorithms,rous missing values, errors, and biased inputs. This is due to under- or over-representation of certain groups of data, trivial data curation methods, etc. In this chapter, we describe Perceptive Bias, Processing Bias, and the ways to compute bias for Medical Big Data analysis.
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