BEHRT: Transformer for Electronic Health Records
BEHRT: Transformer for Electronic Health Records
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DOI：
10.1038/s41598-020-62922-y
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发表时间：
2020-04-28
影响因子：
4.6
通讯作者：
Salimi-Khorshidi, Gholamreza
中科院分区：
文献类型：
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作者：
Li, Yikuan;Rao, Shishir;Salimi-Khorshidi, Gholamreza
今天，尽管医学发展了几十年，人们对精准医疗的兴趣也越来越大，但绝大多数诊断都是在患者开始出现明显的疾病迹象时进行的。然而，疾病的早期指示和检测可以为患者和护理人员提供早期干预、更好的疾病管理和有效分配医疗保健资源的机会。机器学习（包括深度学习）的最新发展为解决这一未满足的需求提供了很好的机会。在这项研究中，我们介绍了BEHRT：一种用于电子健康记录（EHR）的深度神经序列转导模型，能够同时预测未来访问中301种疾病的可能性。在对来自近160万人的数据进行训练和评估时，BEHRT显示出比现有的最先进的深度EHR模型显著提高了8.0-13.2%（就不同任务的平均精度分数而言）。除了其可扩展性和上级准确性外，BEHRT还可以对其预测进行个性化解释;其灵活的架构使其能够整合多个异构概念（例如，诊断、药物治疗、测量等）以进一步提高其预测的准确性;其在疾病和患者表示方面的（预）训练结果可用于未来的研究（即，迁移学习）。
Today, despite decades of developments in medicine and the growing interest in precision healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs of illness. Early indication and detection of diseases, however, can provide patients and carers with the chance of early intervention, better disease management, and efficient allocation of healthcare resources. The latest developments in machine learning (including deep learning) provides a great opportunity to address this unmet need. In this study, we introduce BEHRT: A deep neural sequence transduction model for electronic health records (EHR), capable of simultaneously predicting the likelihood of 301 conditions in one's future visits. When trained and evaluated on the data from nearly 1.6 million individuals, BEHRT shows a striking improvement of 8.0-13.2% (in terms of average precision scores for different tasks), over the existing state-of-the-art deep EHR models. In addition to its scalability and superior accuracy, BEHRT enables personalised interpretation of its predictions; its flexible architecture enables it to incorporate multiple heterogeneous concepts (e.g., diagnosis, medication, measurements, and more) to further improve the accuracy of its predictions; its (pre-)training results in disease and patient representations can be useful for future studies (i.e., transfer learning).