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Titlebook: Explainable Machine Learning in Medicine; Karol Przystalski,Rohit M. Thanki Book 2024 The Editor(s) (if applicable) and The Author(s), und

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發(fā)表于 2025-3-21 19:56:09 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Explainable Machine Learning in Medicine
編輯Karol Przystalski,Rohit M. Thanki
視頻videohttp://file.papertrans.cn/320/319300/319300.mp4
概述Provides a primer on explainable artificial intelligence and machine learning methods that can be used in medical cases.Presents how explainable AI aids in choosing ML algorithms that give better perf
叢書名稱Synthesis Lectures on Engineering, Science, and Technology
圖書封面Titlebook: Explainable Machine Learning in Medicine;  Karol Przystalski,Rohit M. Thanki Book 2024 The Editor(s) (if applicable) and The Author(s), und
描述This book covers a variety of advanced communications technologies that can be used to analyze medical data and can be used to diagnose diseases in clinic centers. The book is a primer of methods for medicine, providing an overview of explainable artificial intelligence (AI) techniques that can be applied in different medical challenges. The authors discuss how to select and apply the proper technology depending on the provided data and the analysis desired. Because a variety of data can be used in the medical field, the book explains how to deal with challenges connected with each type. A number of scenarios are introduced that can happen in real-time environments, with each pared with a type of machine learning that can be used to solve it..
出版日期Book 2024
關(guān)鍵詞Explainable Machine Learning; Medical Machine Learning; Medicine Artificial Intelligence; Medical Data
版次1
doihttps://doi.org/10.1007/978-3-031-44877-5
isbn_softcover978-3-031-44879-9
isbn_ebook978-3-031-44877-5Series ISSN 2690-0300 Series E-ISSN 2690-0327
issn_series 2690-0300
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 20:42:44 | 只看該作者
Medical Tabular Data,simple example of this type of data. We have the weight, age, and height values divided by the person to which they are assigned. Such data might be too simple to be used with a deep neural network or even most shallow methods, but we can use methods such as linear regression to see the correlation
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,Natural Language Processing for?Medical Data Analysis,ledge and the terms that in the case of medicine can be in Latin. This means that most of the publicly available language models are not trained to recognize the medical terms. For each NLP exercise, most of the models are developed in English.
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發(fā)表于 2025-3-22 05:03:23 | 只看該作者
,Computer Vision for?Medical Data Analysis,he medical diagnostics on a daily basis. The most typical are X-ray and ultrasound scans. Some General Practitioners have a dermatoscope to take pictures of the skin. Similarly, thermography can be used to take kind of heatmap images of our body. If the diagnosis of a disease is more complex, magnet
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Critique as a Notion of Spiritualityn observe how the observed organ changes its behavior over time, making it possible to recognize different types of anomalies or diseases compared to data captured in one time, such as typical MRI scans.
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