一个机器学习辅助的系统,利用患者的心率监测数据来预测甲状腺毒性病:一项回顾性队列研究
Kyubo Shin1, Jongchan Kim1, Jaemin Park1
1THYROSCOPE INC., Ulsan, Republic of Korea.
Scientific reports
|November 30, 2023
概括
一个新的机器学习系统使用可穿戴心率 (HR) 数据来预测甲状腺功能障碍患者的甲状腺毒性. 这种非侵入性方法显示出高准确性,有可能优化监测和治疗时间.
科学领域:
- 内分泌学 在内分泌学.
- 医疗技术 医疗技术 医学技术
- 机器学习 机器学习
背景情况:
- 休息心率 (HR) 与甲状腺功能障碍中的血清自由甲状腺素相关.
- 可穿戴设备提供持续的HR监控.
- 甲状腺毒素症需要及时诊断和管理.
研究的目的:
- 开发和评估一个机器学习 (ML) 辅助的系统,用于使用可穿戴的人力资源数据预测甲状腺毒性.
- 在患有甲状腺功能障碍的患者中,评估系统在区分甲状腺毒素与非甲状腺毒素的准确性.
主要方法:
- 在175名甲状腺功能障碍患者中,在4个月内从可穿戴设备收集HR数据.
- 配对HR数据 (测试前10天) 与662个甲状腺功能测试 (TFT) 结果.
- 开发了一个ML系统,使用HR指标 (平均值,SD,斜率,kurtosis) 和Jensen-Shannon分歧进行预测.
主要成果:
- 该ML系统实现了86.14%的灵敏度和85.92%的特异性,用于预测甲状腺毒性病.
- 除了亚临床病例,性能提高到86.14%的灵敏度和98.28%的特异性.
- 关键预测参数包括睡眠HR变化和HR-TFT分布差异的变化.
结论:
- 这种ML辅助系统能够在甲状腺功能障碍患者中准确预测甲状腺毒性.
- 这项技术有可能监测甲状腺状况并优化血液测试时间.
- 进一步收集数据可能会提高预测准确性和临床实用性.
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