论文标题
使用电子健康记录和预处理的深度学习模型来增强疾病结果的预测
Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models
论文作者
论文摘要
问题：在纵向电子健康记录的大数据集上预测的编码器架构是否可以改善患者的结果预测？研究结果：在这项对680万患者的预后研究中，我们对多个结果的序列到序列的预测模型在广泛的患者预后（包括故意的自我伤害和胰腺癌）上超过了最先进的伯特。含义：深层双向和自回归表示可以改善患者的预测预测。
Question: Can an encoder-decoder architecture pretrained on a large dataset of longitudinal electronic health records improves patient outcome predictions? Findings: In this prognostic study of 6.8 million patients, our denoising sequence-to-sequence prediction model of multiple outcomes outperformed state-of-the-art models scuh pretrained BERT on a broad range of patient outcomes, including intentional self-harm and pancreatic cancer. Meaning: Deep bidirectional and autoregressive representation improves patient outcome prediction.
