CATI:在英国生物库研究中,医疗背景增强的诊断代码分配框架.
Yue Shen1, Jie Wang1, Zhe Wang1
1MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, Anhui, 230027, China.
Artificial intelligence in medicine
|May 9, 2025
概括
本研究介绍了CATI,这是一个新的框架,通过整合文本数据和疾病等级来在大型生物库中分配准确的诊断代码. 通过更好的患者队列形成,CATI改进了现有方法,增强了与疾病相关的研究.
科学领域:
- 生物信息学是一种生物信息学.
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 准确的诊断编码对于大规模的生物库研究和下游疾病研究至关重要.
- 当前的方法往往忽视了有价值的文本医学信息和疾病分类系统的等级性质.
- 生物库中缺少的诊断代码限制了患者数据对临床研究的有用性.
研究的目的:
- 开发和评估CATI,一个医疗背景增强的框架,用于改进大型生物库的诊断代码分配.
- 将文本细节和疾病等级整合到一个统一的模型中,以便更准确地编码.
- 解决生物银行数据集中患者缺少诊断代码的挑战.
主要方法:
- CATI框架整合了来自关键特征和疾病等级的文本细节,通过快速调整使用BioBERT的文本嵌入.
- 开发了一种新的卷积层,以有效地在相邻的诊断代码之间传播信息,利用等级结构.
- 这项研究利用了英国生物库的数据,专注于Phecodes和ICD-10代码作为标准疾病格式.
主要成果:
- 与最先进的方法相比,CATI在Phecodes和ICD-10代码分配方面表现优越.
- 在未见疾病代码的平均AUROC中至少实现了5.16%的改善.
- 在有限的培训实例 (1000-10000) 的疾病代码中,平均AUPRC增加了8.68%.
结论:
- 通过从文本数据和疾病层次结构中整合医疗背景,CATI有效地提高了诊断代码的分配.
- 该框架有助于为下游生物医学研究创建明确的队列.
- 通过利用丰富的医疗信息,CATI为复杂的医疗保健任务提供了有价值的方法.
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