机器学习方法与协调的跨国数据集,用于在2型糖尿病患者中增强预测甲状腺功能低下症
Robert P Adelson1, Anurag Garikipati1, Yunfan Zhou1
1Montera, Inc. dba Forta, 548 Market St, PMB 89605, San Francisco, CA 94104-5401, USA.
Diagnostics (Basel, Switzerland)
|June 19, 2024
概括
一个新的机器学习工具可以识别患有2型糖尿病 (T2D) 的患者,这些患者患有并发性甲状腺功能低下症 (HT) 的风险很高. 这种方法有助于确定查的优先级,并通过早期诊断和治疗来改善患者的治疗结果.
科学领域:
- 内分泌学 在内分泌学.
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 2型糖尿病 (T2D) 是一个日益严重的全球健康问题.
- 并发性甲状腺功能低下症 (HT) 恶化T2D并发症,但查不频繁,延迟诊断和治疗.
- 在T2D患者中早期检测HT可以减轻严重的健康风险.
研究的目的:
- 开发一种机器学习算法 (MLA),用于将T2D患者分类为患有并发性甲状腺功能低下症 (HT) 的低风险或高风险.
- 提高T2D患者HT查的效率和及时性.
- 通过早期风险分层来提高临床管理和患者的结果.
主要方法:
- 使用来自NIH All of Us (AoU) 和英国生物银行 (UKBB) 的跨国患者数据开发机器学习算法 (MLA).
- 在综合数据集上对MLA进行培训和验证,以评估其预测性能.
- 评估MLA的负预测值 (NPV) 和接收器操作特征曲线 (AUROC) 下的区域.
主要成果:
- 在合并的数据集上,MLA实现了高负预测值 (NPV) 0.989和AUROC 0.762.
- 在综合数据集上的MLA性能超过了在单个数据集上训练的模型 (AoU:0.666,UKBB:0.622).
- 在MLA培训期间增加数据集多样性显著提高了其预测准确性.
结论:
- 开发的MLA是一种高NPV的临床决策工具,用于识别HT风险较低的T2D患者,从而实现专注的实验室测试.
- 这种自动化工具可以补充标准护理查,允许临床医生优先考虑高风险个体及时干预.
- 在T2D患者中早期识别HT风险有助于量身定制的管理,最终改善患者的健康结果.
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