MIMIC-IV-Ext Triage Instruction Corpus
收藏资源简介：
Emergency department (ED) overcrowding leads to delayed care, increased patient risk, and inefficient resource use. The MIMIC-IV-Ext Triage Instruction Corpus (MIETIC) addresses this by providing 9,629 structured triage cases from MIMIC-IV, aligned with the Emergency Severity Index (ESI). MIETIC supports large language model (LLM) training for AI-assisted triage, improving accuracy, consistency, and risk assessment. The dataset includes chief complaints, vital signs, demographics, and medical history, ensuring realistic triage decision-making. Developed through automated quality control and expert validation, MIETIC enhances model performance in high-risk and moderate-risk classification. Available in CSV formats, MIETIC enables research in clinical NLP, AI-driven triage, and decision-support tools. The dataset module includes: 1. Structured triage cases with ESI labels. 2. Triage case generation prompts for instruction tuning. 3. Expert-validated samples for quality control. 4. SQL scripts for data extraction and validation, hosted on GitHub. MIETIC provides a standardized, reproducible dataset to advance AI-driven emergency triage, optimizing accuracy, efficiency, and resource allocation.
急诊科（Emergency Department, ED）拥挤会引发诊疗延误、患者风险升高以及资源利用低效。 MIMIC-IV分诊指令语料库（MIMIC-IV-Ext Triage Instruction Corpus, MIETIC）正是针对该问题，从MIMIC-IV数据集中提取了9629例结构化分诊病例，并与急诊严重程度指数（Emergency Severity Index, ESI）进行对齐。 MIETIC可支持用于AI辅助分诊的大语言模型（Large Language Model, LLM）训练，提升分诊的准确性、一致性与风险评估能力。该数据集包含患者主诉、生命体征、人口统计学信息以及病史数据，能够保障分诊决策的真实性。本语料库通过自动化质量控制与专家校验流程构建，可提升模型在高危与中危病例分类任务中的性能表现。 MIETIC以CSV格式提供，可用于临床自然语言处理（Natural Language Processing, NLP）、AI驱动分诊以及决策支持工具相关研究。该数据集模块包含： 1. 带有ESI标签的结构化分诊病例； 2. 用于指令微调的分诊病例生成提示词； 3. 用于质量控制的专家校验样本； 4. 用于数据提取与校验的SQL脚本，托管于GitHub平台。 MIETIC提供了一套标准化、可复现的数据集，旨在推动AI驱动的急诊分诊发展，优化诊疗准确性、效率与资源分配。
