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Titlebook: Artificial Intelligence in Medicine; 15th Conference on A John H. Holmes,Riccardo Bellazzi,Niels Peek Conference proceedings 2015 Springer

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发表于 2025-3-21 17:25:15 | 显示全部楼层 |阅读模式
期刊全称Artificial Intelligence in Medicine
期刊简称15th Conference on A
影响因子2023John H. Holmes,Riccardo Bellazzi,Niels Peek
视频video
发行地址Includes supplementary material:
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Artificial Intelligence in Medicine; 15th Conference on A John H. Holmes,Riccardo Bellazzi,Niels Peek Conference proceedings 2015 Springer
影响因子This book constitutes the refereed proceedings of the 15th Conference on Artificial Intelligence in Medicine, AIME 2015, held in Pavia, Italy, in June 2015. The 19 revised full and 24 short papers presented were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections: process mining and phenotyping; data mining and machine learning; temporal data mining; uncertainty and Bayesian networks; text mining; prediction in clinical practice; and knowledge representation and guidelines.
Pindex Conference proceedings 2015
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发表于 2025-3-21 23:48:01 | 显示全部楼层
Miguel Alonso,Alan Kramer,Javier Rodrigonce of the three main “stakeholder” communities: researchers, clinicians and patients/carers. The ability to harness such data is essential for the integration of medical information to support clinical decision making and medical research.
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https://doi.org/10.1007/978-3-658-07764-8od is evaluated on predicting 30-day hospital readmission for pediatric patients from California and provides evidence that a knowledge-based approach outperforms traditional methods and that the newly proposed method is competitive with state-of-the-art methods.
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Kurt Moser,Alfred Schmid,Hermann Leharon local anomalies using a Latent Dirichlet Allocation (LDA)-based probabilistic model. The proposal was evaluated in the study of unstable angina CTP, testing 12,152 patient traces from the Chinese PLA General Hospital.
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https://doi.org/10.1007/978-3-658-30875-9ime intervals where specific order sets are recommended in the CPOE. We evaluate our algorithm using real patient data from a pediatric hospital, and demonstrate that data-driven order sets have the potential to dominate existing, consensus order sets in terms of usability and cognitive workload.
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https://doi.org/10.1007/978-3-658-44750-2s; hence preserves the privacy of the dwellers, reduces costs and eliminates the need to remember to wear a device. Our results indicate a good performance for fall detection with an overall F-score of 94%.
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