Tahayori Bahman, Chini-Foroush Noushin, Akhlaghi Hamed
Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.
Emergency Department, St Vincent's Hospital, Melbourne, Victoria, Australia.
Emerg Med Australas. 2021 Jun;33(3):480-484. doi: 10.1111/1742-6723.13656. Epub 2020 Oct 11.
To demonstrate the potential of machine learning and capability of natural language processing (NLP) to predict disposition of patients based on triage notes in the ED.
A retrospective cohort of ED triage notes from St Vincent's Hospital (Melbourne) was used to develop a deep-learning algorithm that predicts patient disposition. Bidirectional Encoder Representations from Transformers, a recent language representation model developed by Google, was utilised for NLP. Eighty percent of the dataset was used for training the model and 20% was used to test the algorithm performance. Ktrain library, a wrapper for TensorFlow Keras, was employed to develop the model.
The accuracy of the algorithm was 83% and the area under the curve was 0.88. Sensitivity, specificity, precision and F1-score of the algorithm were 72%, 86%, 56% and 63%, respectively.
Machine learning and NLP can be together applied to the ED triage note to predict patient disposition with a high level of accuracy. The algorithm can potentially assist ED clinicians in early identification of patients requiring admission by mitigating the cognitive load, thus optimises resource allocation in EDs.
证明机器学习的潜力以及自然语言处理（NLP）基于急诊科（ED）分诊记录预测患者处置情况的能力。
使用来自圣文森特医院（墨尔本）的急诊科分诊记录回顾性队列来开发一种预测患者处置情况的深度学习算法。采用了谷歌最近开发的语言表征模型——来自变换器的双向编码器表征（BERT）进行自然语言处理。数据集的80%用于训练模型，20%用于测试算法性能。使用Ktrain库（TensorFlow Keras的一个包装器）来开发模型。
该算法的准确率为83%，曲线下面积为0.88。该算法的灵敏度、特异度、阳性预测值和F1分数分别为72%、86%、56%和63%。
机器学习和自然语言处理可以一起应用于急诊科分诊记录，以高精度预测患者处置情况。该算法有可能通过减轻认知负担来帮助急诊科临床医生早期识别需要住院的患者，从而优化急诊科的资源分配。
