Sam Freeman, Isuru Ranapanada, Md Ali Hossain, Kogul Srikandabala, Md Anisur Anisur Rahman, Damminda Alahakoon, Hamed Akhlaghi
{"title":"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。","authors":"Sam Freeman, Isuru Ranapanada, Md Ali Hossain, Kogul Srikandabala, Md Anisur Anisur Rahman, Damminda Alahakoon, Hamed Akhlaghi","doi":"10.1136/bmjhci-2024-101285","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods and analysis: </strong>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.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12314934/pdf/","citationCount":"0","resultStr":"{\"title\":\"Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper.\",\"authors\":\"Sam Freeman, Isuru Ranapanada, Md Ali Hossain, Kogul Srikandabala, Md Anisur Anisur Rahman, Damminda Alahakoon, Hamed Akhlaghi\",\"doi\":\"10.1136/bmjhci-2024-101285\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>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.</p><p><strong>Methods and analysis: </strong>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.</p>\",\"PeriodicalId\":9050,\"journal\":{\"name\":\"BMJ Health & Care Informatics\",\"volume\":\"32 1\",\"pages\":\"\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12314934/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMJ Health & Care Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1136/bmjhci-2024-101285\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMJ Health & Care Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1136/bmjhci-2024-101285","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
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.