使用命名实体识别和可解释的机器学习预测医院再入院情况
Safaa Dafrallah1, Moulay A Akhloufi1
1Perception, Robotics and Intelligent Machines (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1A 3E9, Canada.
Diagnostics (Basel, Switzerland)
|October 16, 2024
概括
使用临床笔记预测早期住院再接收风险可以降低成本并改善患者的治疗结果. 我们的新方法使用命名实体识别和机器学习来实现88.4%的准确性来识别有风险的患者.
科学领域:
- 临床信息学是一种临床信息学.
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 早期住院再接收会带来重大的财务和临床挑战.
- 对再接收风险的预测模型对于患者管理和资源分配至关重要.
- 释放备忘录包含有价值的信息,用于重新接收预测.
研究的目的:
- 开发和评估一种用于预测非计划性医院再入院的新方法.
- 为了利用临床出院说明来加强再接收风险预测.
- 提高再接收预测模型的准确性和可解释性.
主要方法:
- 使用MIMIC-III数据库进行临床出院笔记.
- 采用预训练的命名实体识别 (BioMedical-NER) 模型,该模型基于来自变压器的双向编码器表示 (BERT).
- 训练有素的机器学习模型,包括渐变增强,在提取的特征上进行再接收预测.
主要成果:
- 使用梯度提升算法实现了88.4%的平均精度.
- 与现有的最先进的方法相比,在临床报告中表现出卓越的性能.
- 应用可解释的人工智能 (AI) 技术,提高模型的可解释性.
结论:
- 拟议的方法有效地预测了使用出院笔记的非计划性住院再接收.
- 生物医学-NER和机器学习的整合为临床决策支持提供了强大的工具.
- 可解释的人工智能增强了医疗保健中预测模型的理解和可靠性.
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