论文标题
用于高通量筛查多种疾病的ECG：使用基于人群的数据集的多诊断深度学习的概念证明
ECG for high-throughput screening of multiple diseases: Proof-of-concept using multi-diagnosis deep learning from population-based datasets
论文作者
论文摘要
心电图（ECG）异常与心血管疾病有关，但也可能发生在其他非心血管疾病中，例如精神，神经系统，代谢和感染性疾病。但是，在选定的患者队列中，基于深度学习（DL）的诊断预测的最新成功都仅限于一系列心脏病。在这项研究中，我们使用> 25万名患者> 1000例医疗状况和> 200万个ECG的基于人群的数据集来识别可以准确诊断出可以从患者的第一个医院心电图中准确诊断出的广泛疾病。我们的DL模型发现了128种疾病和68种疾病类别，具有强烈的歧视性能。
Electrocardiogram (ECG) abnormalities are linked to cardiovascular diseases, but may also occur in other non-cardiovascular conditions such as mental, neurological, metabolic and infectious conditions. However, most of the recent success of deep learning (DL) based diagnostic predictions in selected patient cohorts have been limited to a small set of cardiac diseases. In this study, we use a population-based dataset of >250,000 patients with >1000 medical conditions and >2 million ECGs to identify a wide range of diseases that could be accurately diagnosed from the patient's first in-hospital ECG. Our DL models uncovered 128 diseases and 68 disease categories with strong discriminative performance.
