MIMIC-IV-Ext-22MCTS:一个2200万事件的时间临床时间序列数据集,具有风险预测的相对时间
Research square
|December 31, 2025
概括
这项研究介绍了MIMIC-IV-Ext-22MCTS,这是从医疗笔记中提取的临床时间序列事件的大数据集. 这一数据集增强了机器学习模型,以改善医疗保健应用和临床风险预测.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 高质量的时间序列临床事件对于可靠的基于机器学习的医疗风险预测至关重要.
- 像MIMIC-IV-Note这样的现有数据集包含非结构化的放电总结,这给事件提取和时间信息检索带来了挑战.
研究的目的:
- 创建一个全面的临床时间序列事件数据集 (MIMIC-IV-Ext-22MCTS) 与提取的时间信息.
- 开发一个新的框架来处理冗长的出院摘要,并推断临床事件的时间.
- 通过对新数据集进行微调,提高医疗保健应用中的机器学习模型的性能.
主要方法:
- 制定了一个框架,将长长的核准摘要分解成可管理的文本块.
- 利用上下文BM25和语义搜索来识别包含临床事件的相关文本块.
- 使用精心设计的提示符与Llama-3.1-8B模型提取或推断事件时间.
- 从MIMIC-IV-Note提取了22,588,586个临床事件及其相关的时间信息.
主要成果:
- MIMIC-IV-Ext-22MCTS数据集提供了信息丰富和透明的临床时间序列数据.
- 在此数据集上微调BERT的结果是,医疗问题答案的准确性提高了10%,临床试验匹配的准确性提高了3%.
- 在数据集上微调的GPT-2模型显示了临床问题更可靠的临床结果.
结论:
- 拟议的框架有效地从非结构化的出院摘要中提取临床事件和时间信息.
- MIMIC-IV-Ext-22MCTS数据集显著提高了机器学习模型在各种医疗保健任务中的性能.
- 这项工作有助于开发更准确,更可靠的临床风险预测工具.
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