通过在咨询对话中使用知识选技术,提高初步临床诊断的准确性
Ashu Abdul1, Binghong Chen2, Siginamsetty Phani1
1Department of Computer Science and Engineering, SRM University-AP, Neerukonda, Mangalagiri, Guntur Dist., 522503, Andhra Pradesh, India.
Computer methods and programs in biomedicine
|February 1, 2024
概括
知识型诊断变压器 (KDT) 使用深度学习和自然语言处理来从患者症状描述中改进初步临床诊断. 这种人工智能模型达到99%的准确性,大大减少了误诊并改善了患者的治疗结果.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 患者的症状描述往往模糊,导致不准确的初步临床诊断.
- 现有的方法与医学症状报告的模糊性和复杂性作斗争.
- 需要先进的计算模型来提高诊断准确度.
研究的目的:
- 开发和评估一个深度学习模型,即知识型诊断变压器 (KDT),用于基于NLP的初步临床诊断.
- 提高基于患者症状描述的初步诊断的准确性和可靠性.
- 在临床环境中应对患者报告的模糊或不准确症状的挑战.
主要方法:
- 在KDT模型中,使用双边医学知识图 (bMKG) 来提取症状-疾病关系的三倍.
- 使用知识包含-排除方法 (KIA) 来过不良的三倍数并减轻知识噪声.
- 代币嵌入技术与变压器架构相结合,用于疾病预测.
主要成果:
- 一个大规模的普通话医学诊断问答数据集 (MDQA) 创建了260万条目用于培训.
- 该KDT模型还在NIH MedQuAD英语数据集上进行了训练.
- 在各种评估指标中,KDT实现了99%的精度,超过了基线变压器模型.
结论:
- KDT模型在提高初步临床诊断的诊断精度方面表现出显著的有效性.
- 这项研究强调了基于知识的AI和NLP对医疗诊断进行革命的潜力.
- KDT为更准确的诊断提供了途径,减少了错误诊断,并改善了患者的治疗结果.
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