AI knows when you're going to die. But unlike in sci-fi movies, that information could end up saving lives.

人工智能知道你的死亡时间。但和科幻电影不同的是，这些信息最后可能会挽救生命。

A new paper published in Nature suggests that feeding electronic health record data to a deep learning model could substantially improve the accuracy of projected outcomes.

一篇发表在《自然》杂志上的新论文显示，深度学习模型的进食电子健康数据能够大幅提高预测结果的准确性。

In trials using data from two US hospitals, researchers were able to show that these algorithms could predict a patient's length of stay and time of discharge, but also the time of death.

在使用了两家美国医院数据的试验中，研究人员可以证明这些算法不仅能够预测病人的住院天数和出院时间，还能预测死亡时间。

The neural² network described in the study uses an immense amount of data, such as a patient's vitals and medical history, to make its predictions.

研究中所描述的神经网络使用了大量的数据进行预测，比如病人的生命特征和病史。

A new algorithm lines up previous events of each patient's records into a timeline, which allowed the deep learning model to pinpoint³ future outcomes, including time of death.

一种新的算法将每个病人所记录的活动经历排列成一个时间轴，这使得深度学习模型能够确定包括死亡时间在内的未来的结果。

The neural network even includes handwritten notes, comments, and scribbles⁴ on old charts to make its predictions. And all of these calculations are done in record time, of course.

用于预测的神经网络甚至包括在旧图表上手写的笔记、评论和涂鸦。当然，所有的这些计算都是在记录时间内完成的。

What can we do with this information, besides fear the inevitable⁵? Hospitals could find new ways to prioritize patient care, adjust treatment plans, and catch medical emergencies before they even occur.

除了害怕这些不可避免的事情，我们还能用这些信息做什么？医院可以找到新的方法来优先照顾病人，调整治疗方案，并在发生紧急情况之前及时处理医疗事故。

It could also free up healthcare workers, who would no longer have to manipulate the data into a standardized⁶, legible format¹.

它也可以解放医疗工作者，他们不再需要将数据转换成标准化的、更易读取的格式。