一种系统的方法来优先考虑在诊断上有用的发现,以便将其纳入电子健康记录作为离散数据,以改进临床人工智能工具和基因组研究
P Guillod1, A Savvas2, P N Robinson3
1Yale School of Medicine, Yale University, 333 Cedar Street, New Haven, CT 06510, USA.
概括
这项研究开发了一种众包方法,用于识别和优先考虑电子健康记录 (EHR) 的疾病表型. 这种方法通过有效地结构化EHR数据,帮助人工智能 (AI) 在疾病诊断和研究中发挥作用.
科学领域:
- 基因组学和生物信息学
- 临床信息学 临床信息学
- 人工智能在医学中的应用
背景情况:
- 电子健康记录 (EHR) 包含大量数据,但对于人工智能应用而言通常是无结构的.
- 在不干扰临床工作流程的情况下开发记录离散EHR数据的方法至关重要.
- 为记录优先考虑患者特征,提高了医疗保健中的AI实用性.
研究的目的:
- 建议和评估一种方法来识别和优先考虑临床相关的EHRs的表型.
- 提高EHR数据对人工智能 (AI) 工具在疾病研究和诊断中的实用性.
- 专注于区分小B细胞淋巴瘤作为主要应用.
主要方法:
- 一个众包网站被开发用于记录疾病和表型.
- 一个专家委员会标准化了表型术语,结合了人类表型本体学 (HPO) 术语.
- 贝叶斯网络 (BNs) 是使用100个淋巴结活检的离散表型数据来构建的,以评估诊断准确性和表型优先级.
主要成果:
- 确定了146种可能有用的表型;70-75个被纳入贝叶斯网络 (BNs).
- 使用所有表型,BNs在非边缘区域淋巴瘤 (96.3%) 中实现了高诊断准确率,在边缘区域淋巴瘤中达到中度准确率 (50%).
- 精度即使在减少的表型组 (例如,15种表型的非边缘区域淋巴瘤的93.8%).
结论:
- 本试点研究为系统改进电子健康记录数据结构建立了基础方法.
- 生成的数据集作为评估类似数据驱动方法的基准.
- 这些发现支持将优先表型集成到EHR中,以加强AI驱动的医学研究和临床决策.
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