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Aside from the almost-instantaneous warning signs--chest pain, shortness of breath, nausea--heart attacks are notoriously hard to predict. Standard models tend to "oversimplify" cardiovascular disease, reducing risk to eight core baseline variables.
除了猝发的警告信号——胸痛、气促、恶心以外，心脏病是众所周知的难以预测。标准模型似乎让心血管疾病“过分简单化”，将风险降至八个核心的基本变量。
These factors don't automatically foretell a heart attack. Machine learning offers an alternative approach, exploiting big data to minimize human error. Using four computer-learning algorithms, nearly 25,000 fatal or non-fatal cardiovascular events were documented over the study's 10-year period, about 75 percent of which were accurately predicted by the algorithms.
这些因素并不会自动预测心脏病。机器学习提供了另一种方法，利用大数据把人为误差降到最低。通过四种计算机学习算法，在10年研究期间，有近25000例致命或非致命心血管疾病被记录了下来，其中约75%的病例被算法准确预测到。
The neural network formula tested highest, beating existing guidelines by 7.6 percent. In fact, the MVP algorithm correctly identified 355 more patients than human doctors, proving its ability to help save lives.
神经网络公式的预测率最高，超过现有指导方针7.6%。其实，MVP算法比人类医生更准确地预测了355例病情，证明它确实有能力挽救生命。
译文属可可英语原创，未经允许，不得转载。
