医疗转诊临床优先级分类数据集
包含17,378份耳鼻喉科转诊文本，用于训练AI按临床优先级自动分诊。
基本信息
资源简介
医疗转诊临床优先级分类数据集
【一句话概览】
本数据集是 figshare 公开的文本数据集，收录 1 个文件、合计 278.52 KB，主题涵盖 medical referral、electronic health records、clinical decision support system、machine learning、text mining，可用于医学自然语言处理（实体识别、信息抽取等），CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Datasheet1_Artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria.pdf》。配套论文《RETRACTED: Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques》发表于 International Journal of Environmental Research and Public Health（2022），DOI：10.3390/ijerph19127384。源站首次发布：2023-10-27。
【数据内容】
源站原始描述：The clinical prioritisation criteria (CPC) are a clinical decision support tool that ensures patients referred for public specialist outpatient services to Queensland Health are assessed according to their clinical urgency. Medical referrals are manually triaged and prioritised into three categories by the associated health service before appointments are booked. We have developed a method using artificial intelligence to automate the process of categorizing medical referrals based on clinical p…
【数据结构与技术规格】
文件数 1 个，合计 278.52 KB。Datasheet1_Artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria.pdf：application/pdf，278.52 KB。数据集 DOI：10.3389/fdgth.2023.1192975.s001。
【主题与分类】
源站学科分类：Mobile Technologies、Health Information Systems (incl. Surveillance)、Health Informatics、Innovation and Technology Management。主题标签：medical referral、electronic health records、clinical decision support system、machine learning、text mining、natural language processing、artificial intelligence。
【适用方向】
文本类数据可用于医学自然语言处理，如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面：https://frontiersin.figshare.com/articles/dataset/Datasheet1_Artificial_intelligence_in_medical_referrals_triage_based_on_Clinical_Prioritization_Criteria_pdf/24449536
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Abdel-Hafez, Ahmad; Jones, Melanie; Ebrahimabadi, Maziiar; Ryan, Cathi; Graham, Steve; Slee, Nicola; et al. (2023). Datasheet1_Artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria.pdf. Frontiers. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Datasheet1_Artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria.pdf
- DOI：10.3389/fdgth.2023.1192975.s001
- 发布日期：2023-10-27
- 文件数：1 个
- 文件总体积：278.52 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Datasheet1_Artificial_intelligence_in_medical_referrals_triage_based_on_Clinical_Prioritization_Criteria_pdf/24449536
命令行下载
curl -L -o "Datasheet1_Artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria.pdf" "https://ndownloader.figshare.com/files/42929020"
文件清单
Datasheet1_Artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria.pdf（278.52 KB）
文件总体积 278.52 KB（源站 API 实测）。
数据说明
The clinical prioritisation criteria (CPC) are a clinical decision support tool that ensures patients referred for public specialist outpatient services to Queensland Health are assessed according to their clinical urgency. Medical referrals are manually triaged and prioritised into three categories by the associated health service before appointments are booked. We have developed a method using artificial intelligence to automate the process of categorizing medical referrals based on clinical prioritization criteria (CPC) guidelines. Using machine learning techniques, we have created a tool that can assist clinicians in sorting through the substantial number of referrals they receive each year, leading to more efficient use of clinical specialists’’ time and improved access to healthcare for patients. Our research included analyzing 17,378 ENT referrals from two hospitals in Queensland between 2019 and 2022. Our results show a level of agreement between referral categories and generated predictions of 53.8%.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fdgth.2023.1192975.s001（source: https://frontiersin.figshare.com/articles/dataset/Datasheet1_Artificial_intelligence_in_medical_referrals_triage_based_on_Clinical_Prioritization_Criteria_pdf/24449536）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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