| 書目名稱 | Efficacy Analysis in Clinical Trials an Update |
| 副標(biāo)題 | Efficacy Analysis in |
| 編輯 | Ton J. Cleophas,Aeilko H. Zwinderman |
| 視頻video | http://file.papertrans.cn/303/302925/302925.mp4 |
| 概述 | It shows, for the first time, that machine learning methodologies can be used for assessing efficacy data of controlled clinical trials.It confirms, that machine learning methodologies provide better |
| 圖書封面 |  |
| 描述 | .Machine learning and big data is hot. It is, however, virtually unused in clinical trials. This is so, because randomization is applied to even out multiple variables..Modern medical computer files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required..This is the first publication of clinical trials that have been systematically analyzed with machine learning. In addition, all of the machine learning analyses were tested against traditional analyses. Step by step statistics for self-assessments are included..The authors conclude, that machine learning is often more informative, and provides better sensitivities of testing than traditional analytic methods do. |
| 出版日期 | Textbook 2019 |
| 關(guān)鍵詞 | Clinical trials; Traditional efficacy analysis; Machine learning for efficacy analysis; Data mining; Big |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-19918-0 |
| isbn_softcover | 978-3-030-19920-3 |
| isbn_ebook | 978-3-030-19918-0 |
| copyright | Springer Nature Switzerland AG 2019 |
|Archiver|手機(jī)版|小黑屋|
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