Freeman Sam, Ranapanada Isuru, Hossain Md Ali, Srikandabala Kogul, Rahman Md Anisur Anisur, Alahakoon Damminda, Akhlaghi Hamed
Emergency Department, St Vincent's Hospital (Melbourne) Limited, Fitzroy, Victoria, Australia
Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
BMJ Health Care Inform. 2025 Jul 31;32(1):e101285. doi: 10.1136/bmjhci-2024-101285.
To address timely care in emergency departments, artificial neural networks (ANNs) with natural language processing will be applied to triage notes to predict patient disposition. This study will develop a predictive model that predicts disposition and type of admission.
This will include data preprocessing and quality enhancement, masked language modelling, ANN-based fusion network for prediction. Generative artificial intelligence, along with a medical dictionary, will be employed to augment and contextually reconstruct triage notes to disambiguate and improve linguistic quality. Text features will be extracted, and cluster analysis will be performed on the extracted topics and text features to identify distinct patterns.
为了在急诊科实现及时护理，将应用具有自然语言处理功能的人工神经网络（ANN）对分诊记录进行分析，以预测患者的处置情况。本研究将开发一种预测模型，用于预测处置方式和入院类型。
这将包括数据预处理和质量提升、掩码语言建模、基于人工神经网络的融合网络预测。生成式人工智能将与医学词典一起用于扩充和上下文重构分诊记录，以消除歧义并提高语言质量。将提取文本特征，并对提取的主题和文本特征进行聚类分析，以识别不同的模式。
