Si Yuqi, Roberts Kirk
School of Biomedical Informatics, The University of Texas Health Science Center at Houston Houston, TX, USA.
AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:597-606. eCollection 2020.
To explicitly learn patient representations from longitudinal clinical notes, we propose a hierarchical attention-based recurrent neural network (RNN) with greedy segmentation to distinguish between shorter and longer, more meaningful gaps between notes. The proposed model is evaluated for both a direct clinical prediction task (mortality) and as a transfer learning pre-training model to downstream evaluation (phenotype prediction of obesity and its comorbidities). Experimental results first show the proposed model with appropriate segmentation achieved the best performance on mortality prediction, indicating the effectiveness of hierarchical RNNs in dealing with longitudinal clinical text. Attention weights from the models highlight those parts of notes with the largest impact on mortality prediction and hopefully provide a degree of interpretability. Following the transfer learning approach, we also demonstrate the effectiveness and generalizability of pre-trained patient representations on target tasks of phenotyping.
为了从纵向临床记录中明确学习患者表征，我们提出了一种基于分层注意力的循环神经网络（RNN），并采用贪婪分割来区分记录之间较短和较长、更有意义的间隔。所提出的模型针对直接临床预测任务（死亡率）以及作为下游评估的迁移学习预训练模型（肥胖及其合并症的表型预测）进行了评估。实验结果首先表明，具有适当分割的所提出模型在死亡率预测方面取得了最佳性能，这表明分层RNN在处理纵向临床文本方面的有效性。模型的注意力权重突出了对死亡率预测影响最大的记录部分，并有望提供一定程度的可解释性。遵循迁移学习方法，我们还展示了预训练患者表征在表型目标任务上的有效性和通用性。
