机器学习来预测甲状腺结节的发生:朝着一种定量方法来明智地利用甲状腺超声波的方法
Qijun Liang1, Zhenhong Qi1, Yike Li2
1Health Management Center, Foshan Hospital of Traditional Chinese Medicine, Foshan, Guangdong, China.
Frontiers in endocrinology
|May 22, 2024
概括
机器学习模型可以使用临床数据预测甲状腺结节风险,帮助早期检测和个性化查. 这种方法有助于优化超声波的使用,以获得更好的患者结果.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 内分泌学 在内分泌学.
背景情况:
- 甲状腺超声波对于早期发现结节至关重要,但缺乏常规查的临床指导方针.
- 机器学习 (ML) 提供了使用大型数据集预测临床结果的潜力.
- 这项研究解决了对甲状腺结节风险评估的数据驱动方法的需求.
研究的目的:
- 开发和验证机器学习模型,用于预测甲状腺结节的三年风险.
- 根据临床和实验室数据评估各种ML算法的预测性能.
- 创建一个临床工具 (nomogram) 用于甲状腺结节的实际风险分层.
主要方法:
- 利用了从4386名接受常规体检 (2013-2023) 的中国个人获得的数据.
- 使用五倍交叉验证训练和测试了四个ML算法 (逻辑回归,随机森林,XGBoost,LGBM).
- 评估特征的重要性,并为临床风险评估开发了一个名谱.
主要成果:
- 在3年内,在54.8%的受试者中检测到甲状腺结节.
- 所有的ML模型显著优于基线模型,预测大约三分之二的个体出现结节.
- 关键预测因素包括年龄,高密度脂蛋白,禁食血糖和肌素水平.
结论:
- 基于ML的方法是可行的预测甲状腺结节的发生.
- 机器学习模型可以识别高风险个体进行个性化查.
- 这一策略支持在临床实践中合理使用甲状腺超声波.
关键词:
人工智能的人工智能是人工智能.后勤模型 后勤模型机器学习是机器学习.诺莫格拉姆 (Nomograms) 是一个名字.精准医学是一门精准医学.随机的森林随机的森林甲状腺结节 甲状腺结节超声波学 超声波学 超声波学更多相关视频
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