改进大型紧急911数据报告系统的可用性:使用紧急事件描述的机器学习案例研究

IF 3.9 2区 工程技术 Q1 ERGONOMICS
N. Katherine Yoon , Tyler D. Quinn , Alexa Furek , Nora Y. Payne , Emily J. Haas
{"title":"改进大型紧急911数据报告系统的可用性:使用紧急事件描述的机器学习案例研究","authors":"N. Katherine Yoon ,&nbsp;Tyler D. Quinn ,&nbsp;Alexa Furek ,&nbsp;Nora Y. Payne ,&nbsp;Emily J. Haas","doi":"10.1016/j.jsr.2025.04.001","DOIUrl":null,"url":null,"abstract":"<div><div><em>Introduction:</em> Emergency 9-1-1 incident data are recorded voluntarily within fire-department-specific computer-aided dispatch systems. The National Fire Incident Reporting System serves as a repository for these data, but inconsistency and variability in reporting practices across departments often lead to challenges in data quality and utility. This study aims to enhance emergency incident categorization and explore the feasibility of an automated system using free-text incident data from the National Fire Operations Reporting System (NFORS). <em>Method:</em> Researchers extracted and standardized 3,564 unique 9–1-1 incident descriptions from six fire departments using NFORS data, including narrative fields from emergency reports. The data were preprocessed using natural language processing (NLP) techniques, such as tokenization, stop word removal, and feature extraction (e.g., TF-IDF and n-grams). These features were used to train and evaluate Machine Learning (ML) models, including Naïve Bayes, Random Forest, and Support Vector Machine, to classify incidents into nine categories. The NLP techniques prepared the text data for the ML models, which performed the classification and assessed the automated system’s performance. <em>Results:</em> The study demonstrated significant improvements in incident categorization accuracy using the NLP and ML approach. Unigram models achieved 93% accuracy when applied to 3,564 unique incident descriptions. This performance was evaluated by comparing the automated classifications to manually assigned categories, which served as the reference. Mis-categorizations primarily occurred with “Emergency Medical Services (EMS).” <em>Conclusions:</em> Standardized and consistent incident categorization is vital for informed decision-making, efficient resource allocation, and effective emergency response. Our findings suggest that adopting a robust categorization system, such as the nine-category model using NLP and ML, can improve categorization accuracy and enhance data quality and utility for decision-making. <em>Practical Applications:</em> Public safety agencies can leverage these insights to modernize data systems, strengthen occupational surveillance, and create more resilient and sustainable public safety data systems.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"93 ","pages":"Pages 335-341"},"PeriodicalIF":3.9000,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving the usability of large emergency 911 data reporting systems: A machine learning case study using emergency incident descriptions\",\"authors\":\"N. Katherine Yoon ,&nbsp;Tyler D. Quinn ,&nbsp;Alexa Furek ,&nbsp;Nora Y. Payne ,&nbsp;Emily J. Haas\",\"doi\":\"10.1016/j.jsr.2025.04.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div><em>Introduction:</em> Emergency 9-1-1 incident data are recorded voluntarily within fire-department-specific computer-aided dispatch systems. The National Fire Incident Reporting System serves as a repository for these data, but inconsistency and variability in reporting practices across departments often lead to challenges in data quality and utility. This study aims to enhance emergency incident categorization and explore the feasibility of an automated system using free-text incident data from the National Fire Operations Reporting System (NFORS). <em>Method:</em> Researchers extracted and standardized 3,564 unique 9–1-1 incident descriptions from six fire departments using NFORS data, including narrative fields from emergency reports. The data were preprocessed using natural language processing (NLP) techniques, such as tokenization, stop word removal, and feature extraction (e.g., TF-IDF and n-grams). These features were used to train and evaluate Machine Learning (ML) models, including Naïve Bayes, Random Forest, and Support Vector Machine, to classify incidents into nine categories. The NLP techniques prepared the text data for the ML models, which performed the classification and assessed the automated system’s performance. <em>Results:</em> The study demonstrated significant improvements in incident categorization accuracy using the NLP and ML approach. Unigram models achieved 93% accuracy when applied to 3,564 unique incident descriptions. This performance was evaluated by comparing the automated classifications to manually assigned categories, which served as the reference. Mis-categorizations primarily occurred with “Emergency Medical Services (EMS).” <em>Conclusions:</em> Standardized and consistent incident categorization is vital for informed decision-making, efficient resource allocation, and effective emergency response. Our findings suggest that adopting a robust categorization system, such as the nine-category model using NLP and ML, can improve categorization accuracy and enhance data quality and utility for decision-making. <em>Practical Applications:</em> Public safety agencies can leverage these insights to modernize data systems, strengthen occupational surveillance, and create more resilient and sustainable public safety data systems.</div></div>\",\"PeriodicalId\":48224,\"journal\":{\"name\":\"Journal of Safety Research\",\"volume\":\"93 \",\"pages\":\"Pages 335-341\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Safety Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0022437525000593\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ERGONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Safety Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022437525000593","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ERGONOMICS","Score":null,"Total":0}
引用次数: 0

摘要

简介:紧急911事件数据在消防部门特定的计算机辅助调度系统中自动记录。国家火灾事故报告系统作为这些数据的存储库,但各部门报告实践的不一致性和可变性往往导致数据质量和实用性方面的挑战。本研究旨在加强紧急事件分类,并探索使用国家消防行动报告系统(NFORS)的自由文本事件数据的自动化系统的可行性。方法:研究人员使用NFORS数据从六个消防部门提取并标准化了3,564个独特的911事件描述,包括紧急报告中的叙述字段。使用自然语言处理(NLP)技术对数据进行预处理,例如标记化,停止词去除和特征提取(例如TF-IDF和n-gram)。这些特征用于训练和评估机器学习(ML)模型,包括Naïve贝叶斯,随机森林和支持向量机,将事件分为九类。NLP技术为ML模型准备文本数据,ML模型执行分类并评估自动化系统的性能。结果:研究表明,使用NLP和ML方法显著提高了事件分类的准确性。Unigram模型在应用于3,564个唯一事件描述时达到了93%的准确率。通过将自动分类与作为参考的手动分配的分类进行比较来评估这种性能。分类错误主要发生在“紧急医疗服务(EMS)”。结论:标准化和一致的事件分类对于明智的决策、有效的资源分配和有效的应急响应至关重要。我们的研究结果表明,采用鲁棒的分类系统,如使用NLP和ML的九类模型,可以提高分类精度,提高数据质量和决策效用。实际应用:公共安全机构可以利用这些见解实现数据系统的现代化,加强职业监督,并创建更具弹性和可持续性的公共安全数据系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving the usability of large emergency 911 data reporting systems: A machine learning case study using emergency incident descriptions
Introduction: Emergency 9-1-1 incident data are recorded voluntarily within fire-department-specific computer-aided dispatch systems. The National Fire Incident Reporting System serves as a repository for these data, but inconsistency and variability in reporting practices across departments often lead to challenges in data quality and utility. This study aims to enhance emergency incident categorization and explore the feasibility of an automated system using free-text incident data from the National Fire Operations Reporting System (NFORS). Method: Researchers extracted and standardized 3,564 unique 9–1-1 incident descriptions from six fire departments using NFORS data, including narrative fields from emergency reports. The data were preprocessed using natural language processing (NLP) techniques, such as tokenization, stop word removal, and feature extraction (e.g., TF-IDF and n-grams). These features were used to train and evaluate Machine Learning (ML) models, including Naïve Bayes, Random Forest, and Support Vector Machine, to classify incidents into nine categories. The NLP techniques prepared the text data for the ML models, which performed the classification and assessed the automated system’s performance. Results: The study demonstrated significant improvements in incident categorization accuracy using the NLP and ML approach. Unigram models achieved 93% accuracy when applied to 3,564 unique incident descriptions. This performance was evaluated by comparing the automated classifications to manually assigned categories, which served as the reference. Mis-categorizations primarily occurred with “Emergency Medical Services (EMS).” Conclusions: Standardized and consistent incident categorization is vital for informed decision-making, efficient resource allocation, and effective emergency response. Our findings suggest that adopting a robust categorization system, such as the nine-category model using NLP and ML, can improve categorization accuracy and enhance data quality and utility for decision-making. Practical Applications: Public safety agencies can leverage these insights to modernize data systems, strengthen occupational surveillance, and create more resilient and sustainable public safety data systems.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.40
自引率
4.90%
发文量
174
审稿时长
61 days
期刊介绍: Journal of Safety Research is an interdisciplinary publication that provides for the exchange of ideas and scientific evidence capturing studies through research in all areas of safety and health, including traffic, workplace, home, and community. This forum invites research using rigorous methodologies, encourages translational research, and engages the global scientific community through various partnerships (e.g., this outreach includes highlighting some of the latest findings from the U.S. Centers for Disease Control and Prevention).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信