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Titlebook: Artificial Intelligence in Medicine; 17th Conference on A David Ria?o,Szymon Wilk,Annette ten Teije Conference proceedings 2019 Springer Na

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發(fā)表于 2025-3-21 16:28:42 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Artificial Intelligence in Medicine
期刊簡稱17th Conference on A
影響因子2023David Ria?o,Szymon Wilk,Annette ten Teije
視頻videohttp://file.papertrans.cn/163/162487/162487.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Artificial Intelligence in Medicine; 17th Conference on A David Ria?o,Szymon Wilk,Annette ten Teije Conference proceedings 2019 Springer Na
影響因子This book constitutes the refereed proceedings of the 17th Conference on?Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019.. The 22 revised full and 31 short papers presented were carefully?reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.
Pindex Conference proceedings 2019
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Fascism in Italian Cinema since 1945cision medicine, help detect disease before they manifest and support independent living for the elderly, amongst many other things. However, this progress will not be without challenges from both an ethical and privacy standpoint. These issues need understanding from policy makers and developers al
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Foreign Policy up to the Spanish Civil War,ntelligence (AI) open up a possibility for the laymen to perform this procedure by himself in home environment. However, to make it feasible, the system needs to include two main components: an algorithm for fast and accurate detection of breath phenomena in stethoscope recordings and an AI agent th
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The Fascist Regime: The Lateran Agreements,n be used to alert and request help when this type of accident happens. Certain types of these systems include wearable devices that analyze bio-medical signals from the person carrying it in real time. In this way, Deep Learning algorithms could automate and improve the detection of unintentional f
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https://doi.org/10.1007/978-0-230-59913-0ly trained clinicians, and is a procedure that is known to have relatively low inter-rater agreement (IRA). Moreover, the volume of the data and the rate at which new data becomes available make manual interpretation a time-consuming, resource-hungry, and expensive process. In contrast, automated an
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