用于预测并发症和评估药物疗效的机器学习技术Machine learning techniques for predicting complications and evaluating drugs efficacy |
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课程网址: | http://videolectures.net/mlpmsummerschool2013_rosen_zvi_drugs_eff... |
主讲教师: | Michal Rosen-Zvi |
开课单位: | IBM海法研究实验室 |
开课时间: | 2014-05-13 |
课程语种: | 英语 |
中文简介: | 在本次演讲中,我们将重点讨论机器学习方法对医疗保健的潜在贡献,并将重点放在称为真实世界证据或发布后监测的新趋势上。我们回顾了监督学习的机器学习范式。讨论了集合方法、生成和判别预测算法的价值。将审查如何利用这些方法做出更明智的医疗决策的示例。最后,谈话包括对来自三个不同疾病地区的纵向患者数据进行研究的结果。1.对5万名欧洲HIV患者数据的研究。2.美国糖尿病患者研究和3。对100多万癫痫患者的分析。 |
课程简介: | In this talk we focus on potential contribution of machine learning methods to healthcare and focus on the somewhat new trend called real world evidence or post launch monitoring. We review the machine learning paradigm of supervised learning. The value of ensemble methods and generative and discriminative prediction algorithms is discussed. Examples of how these methods can be utilized for making better informed medical decision will be reviewed. Finally, the talk includes results of studies performed on longitudinal patients' data of patients from three different disease areas. 1. Studies of data of 50K European HIV patients. 2. Studies of American diabetic patients and 3. An analysis of more than 1M epilepsy patients. |
关 键 词: | 机器学习方法; 医疗保健; 预测算法 |
课程来源: | 视频讲座网 |
数据采集: | 2021-12-11:zkj |
最后编审: | 2021-12-11:zkj |
阅读次数: | 38 |