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Titlebook: Machine Learning in Medicine - a Complete Overview; Ton J. Cleophas,Aeilko H. Zwinderman Textbook 20151st edition Springer International P

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發(fā)表于 2025-3-21 16:51:19 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Machine Learning in Medicine - a Complete Overview
編輯Ton J. Cleophas,Aeilko H. Zwinderman
視頻videohttp://file.papertrans.cn/621/620696/620696.mp4
概述First publication of a complete overview of machine learning methodologies for the medical and health sector.Written as a training companion, and as a must-read, not only for physicians and students,
圖書(shū)封面Titlebook: Machine Learning in Medicine - a Complete Overview;  Ton J. Cleophas,Aeilko H. Zwinderman Textbook 20151st edition Springer International P
描述.The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector. It was written as a training companion and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care. In eighty chapters eighty different machine learning methodologies are reviewed, in combination with data examples for self-assessment. Each chapter can be studied without the need to consult other chapters..The amount of data stored in the world‘s databases doubles every 20 months, and clinicians, familiar with traditional statistical methods, are at a loss to analyze them. Traditional methods have, indeed, difficulty to identify outliers in large datasets, and to find patterns in big data and data with multiple exposure / outcome variables. In addition, analysis-rules for surveys and questionnaires, which are currently common methods of data collection, are, essentially, missing. Fortunately, the new discipline, machine learning, is able to cover all of these limitations..So far medical professionals have been rather reluctant to use machine learning. Also, in the fiel
出版日期Textbook 20151st edition
關(guān)鍵詞Coputer science; Data mining; Machine learning; SPSS statistical software; various data mining software
版次1
doihttps://doi.org/10.1007/978-3-319-15195-3
isbn_softcover978-3-319-38638-6
isbn_ebook978-3-319-15195-3
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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978-3-319-38638-6Springer International Publishing Switzerland 2015
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Hierarchical Clustering and K-Means Clustering to Identify Subgroups in Surveys (50 Patients)Clusters are subgroups in a survey estimated by the distances between the values needed to connect the patients, otherwise called cases. It is an important methodology in explorative data mining.
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Density-Based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data (50 Patients)Clusters are subgroups in a survey estimated by the distances between the values needed to connect the patients, otherwise called cases. It is an important methodology in explorative data mining. Density-based clustering is used.
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Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships in Individual Future PatiTo assess whether two step clustering of survey data can be trained to identify subgroups and subgroup membership.
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