论文标题
转移学习以增强癌症和生育数据中的闭经状态预测
Transfer learning to enhance amenorrhea status prediction in cancer and fertility data with missing values
论文作者
论文摘要
很难收集足够标记的培训数据来解决健康和医疗问题（Antropova等，2018）。同样，在健康和医疗数据集中，缺失值是不可避免的，并且解决了由于实例不足和缺失而引起的问题并不简单（Snell等人，2017年，Sterne等人，2009年）。但是，机器学习算法在许多实际的医疗保健问题（例如回归和分类）中取得了重大成功，这些技术可能是解决问题的一种方法。
Collecting sufficient labelled training data for health and medical problems is difficult (Antropova, et al., 2018). Also, missing values are unavoidable in health and medical datasets and tackling the problem arising from the inadequate instances and missingness is not straightforward (Snell, et al. 2017, Sterne, et al. 2009). However, machine learning algorithms have achieved significant success in many real-world healthcare problems, such as regression and classification and these techniques could possibly be a way to resolve the issues.
