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首页> 外文期刊>BMC Medical Informatics and Decision Making >Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records
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Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records

机译：使用电子病历通过长时记忆递归神经网络预测寿命

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Life expectancy is one of the most important factors in end-of-life decision making. Good prognostication for example helps to determine the course of treatment and helps to anticipate the procurement of health care services and facilities, or more broadly: facilitates Advance Care Planning. Advance Care Planning improves the quality of the final phase of life by stimulating doctors to explore the preferences for end-of-life care with their patients, and people close to the patients. Physicians, however, tend to overestimate life expectancy, and miss the window of opportunity to initiate Advance Care Planning. This research tests the potential of using machine learning and natural language processing techniques for predicting life expectancy from electronic medical records. We approached the task of predicting life expectancy as a supervised machine learning task. We trained and tested a long short-term memory recurrent neural network on the medical records of deceased patients. We developed the model with a ten-fold cross-validation procedure, and evaluated its performance on a held-out set of test data. We compared the performance of a model which does not use text features (baseline model) to the performance of a model which uses features extracted from the free texts of the medical records (keyword model), and to doctors’ performance on a similar task as described in scientific literature. Both doctors and the baseline model were correct in 20% of the cases, taking a margin of 33% around the actual life expectancy as the target. The keyword model, in comparison, attained an accuracy of 29% with its prognoses. While doctors overestimated life expectancy in 63% of the incorrect prognoses, which harms anticipation to appropriate end-of-life care, the keyword model overestimated life expectancy in only 31% of the incorrect prognoses. Prognostication of life expectancy is difficult for humans. Our research shows that machine learning and natural language processing techniques offer a feasible and promising approach to predicting life expectancy. The research has potential for real-life applications, such as supporting timely recognition of the right moment to start Advance Care Planning.
机译：预期寿命是生命周期决策中最重要的因素之一。例如，良好的预后有助于确定治疗过程，并有助于预期医疗保健服务和设施的采购，或更广泛地说：有助于进行预先护理计划。预先护理计划通过刺激医生探索对患者以及与患者关系密切的人进行临终护理的偏好，从而提高了生命的最后阶段的质量。但是，医师往往高估了预期寿命，并且错过了启动“预先护理计划”的机会之窗。这项研究测试了使用机器学习和自然语言处理技术从电子病历中预测预期寿命的潜力。我们将预测预期寿命的任务作为有监督的机器学习任务来处理。我们在死者的病历上训练并测试了一个长期的短期记忆循环神经网络。我们使用十倍交叉验证程序开发了该模型，并根据一组保留的测试数据评估了其性能。我们将不使用文本特征的模型（基准模型）的性能与使用从病历的自由文本中提取的特征的特征（关键字模型）的性能以及医生在类似任务上的性能进行了比较。在科学文献中有描述。医生和基线模型在20％的病例中都是正确的，以实际预期寿命的33％左右的余量为目标。相比之下，关键字模型的预测准确率达到29％。虽然医生高估了63％的错误预后寿命，这损害了对适当的临终护理的预期，但是关键字模型高估了仅31％的错误预后寿命。预期寿命对人类来说很难。我们的研究表明，机器学习和自然语言处理技术为预测预期寿命提供了一种可行且有希望的方法。该研究对于现实生活中的应用具有潜力，例如支持及时识别正确的时机以启动高级护理计划。

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