使用定制的NLP模型预测急诊科处置的多地点研究:协议文件。

IF 4.4 Q1 HEALTH CARE SCIENCES & SERVICES
Sam Freeman, Isuru Ranapanada, Md Ali Hossain, Kogul Srikandabala, Md Anisur Anisur Rahman, Damminda Alahakoon, Hamed Akhlaghi
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引用次数: 0

摘要

简介:为了解决急诊科的及时护理问题,人工神经网络(ann)与自然语言处理将应用于分诊记录,以预测患者的处置。本研究将建立一个预测模型来预测患者的性格和入院类型。方法和分析:这将包括数据预处理和质量增强,掩模语言建模,基于人工神经网络的融合网络预测。生成式人工智能以及医学词典将用于增强和上下文重建分类笔记,以消除歧义并提高语言质量。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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper.

Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper.

Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper.

Introduction: 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.

Methods and analysis: 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.

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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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