基于检索的诊断决策支持:混合方法研究研究
Tassallah Abdullahi1, Laura Mercurio2, Ritambhara Singh1,3
1Department of Computer Science, Brown University, Providence, RI, United States.
JMIR medical informatics
|June 19, 2024
概括
这项研究介绍了CliniqIR,这是一个信息检索 (IR) 框架,可以增强诊断决策支持,特别是对于数据有限的罕见疾病. 开发的整体模型通过结合检索和监督方法来实现最先进的诊断预测.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
背景情况:
- 诊断错误显著影响患者的安全和死亡率.
- 机器学习 (ML) 有望通过使用电子健康记录来提高诊断准确性.
- 现有的ML模型往往忽略了有限的可用培训数据的疾病,阻碍了广泛的诊断支持.
研究的目的:
- 开发一个基于信息检索 (IR) 的框架,CliniqIR,以解决诊断中的数据稀疏性.
- 促进更广泛的诊断决策支持,特别是对于罕见或代表性不足的疾病.
- 创建一个可以适应各种IR框架的系统,包括密集和稀疏的检索方法.
主要方法:
- 使用临床文本,UMLS Metathesaurus和PubMed摘要开发了CliniqIR,用于广泛的诊断分类.
- 采用密集和稀疏检索技术实施了CliniqIR.
- 将CliniqIR与ClinicalBERT (变压器的临床双向编码器表示) 在监督和零射击环境中进行比较.
- 创建了一个整体框架,将监督的ClinicalBERT和CliniqIR结合起来,以实现卓越的性能.
主要成果:
- 在没有训练数据的DC3数据集上,CliniqIR在前3个预测中确定了正确的诊断.
- 在MIMIC-III数据集上,CliniqIR的表现优于ClinicalBERT的表现,诊断的培训样本少于5个 (平均值为5个培训样本). 的MRR差异为0.10).
- 在零射击评估中,CliniqIR超过了预训练过的变压器模型,平均互惠等级 (MRR) 至少为0.10.
- 与单个组件相比,整体框架显示了更高的诊断预测准确性.
结论:
- 信息检索 (IR) 对于利用非结构化数据来诊断罕见疾病至关重要.
- 整体框架有效地结合了基于监督和检索的模型,提供了全面的诊断能力.
- 这种方法扩大了诊断决策支持系统的范围,包括不经常遇到的情况.
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