标题： 医学中的贝叶斯LSTM 显示英文标题

标题： Bayesian LSTMs in medicine

摘要： 医学领域有望从近期深度学习的进步中获得显著的好处。 了解任何机器学习算法在决策中存在的不确定性对医疗从业者至关重要。 本研究展示了使用贝叶斯LSTM对医学时间序列进行分类的实用性。 使用四个医学时间序列数据集来展示贝叶斯LSTM相比标准LSTM提供的准确性提升。 此外，我们展示了精心挑选的医学时间序列的自信和不确定分类示例。 通过简单修改深度学习的常规做法，可以为医疗从业者和患者带来显著改进。 显示英文摘要

摘要： The medical field stands to see significant benefits from the recent advances in deep learning. Knowing the uncertainty in the decision made by any machine learning algorithm is of utmost importance for medical practitioners. This study demonstrates the utility of using Bayesian LSTMs for classification of medical time series. Four medical time series datasets are used to show the accuracy improvement Bayesian LSTMs provide over standard LSTMs. Moreover, we show cherry-picked examples of confident and uncertain classifications of the medical time series. With simple modifications of the common practice for deep learning, significant improvements can be made for the medical practitioner and patient.