Understanding a patient’s current status, anticipated clinical course, and likely outcomes can be critical to the practice of medicine. Among patients who are comatose after resuscitation from cardiac arrest, identifying those with potential for awakening and favorable recovery is challenging. Currently, this task is accomplished through the acquisition of one or more diagnostic modalities that aim to assess brain function, with expert interpretation of the test results. This approach is subjective, imprecise, and not scalable. We propose an automatic ensemble classification framework, named SmartPrognosis, to identify comatose post-arrest patients with no recovery potential. SmartPrognosis automatically generates and assembles candidate machine learning pipelines with high sensitivity predicting poor outcomes at a fixed near-zero error rate of misclassifying patients with good outcomes. We demonstrate the effectiveness of SmartPrognosis on real patient data, showing that it over-performs commonly used alternative approaches on all evaluation metrics.

了解患者的当前状况，预期的临床过程以及可能的结果对于医学实践至关重要。在心脏骤停复苏后昏迷的患者中，确定具有唤醒和良好恢复潜力的患者具有挑战性。当前，这项任务是通过获取一种或多种旨在评估脑功能的诊断方式以及对测试结果的专业解释来完成的。这种方法是主观的，不精确的且不可扩展的。我们提出了一个名为SmartPrognosis的自动集成分类框架，以识别没有恢复潜力的昏迷后逮捕患者。SmartPrognosis会自动生成并组装具有高灵敏度的候选机器学习管道，以固定的接近零错误率对不良结果进行错误分类，从而将不良结果分类为良好结果，从而预测不良结果。我们证明了SmartPrognosis对真实患者数据的有效性，表明它在所有评估指标上均优于常用的替代方法。