常规的尿路生物化学不能准确预测结石的类型,也不能准确预测结石形成者的复发:一个多中心,多模型,外部验证的机器学习研究
Robert M Geraghty1, Ian Wilson2, Eric Olinger3
1Department of Urology, Freeman Hospital, Newcastle Upon Tyne, United Kingdom.
Journal of endourology
|October 13, 2023
概括
机器学习模型可以使用尿路生物化学来区分结石类型. 然而,仅凭目前的数据,预测石复发仍然具有挑战性,需要进一步研究新型风险因素.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 生物化学 生物化学
- 数据科学数据科学数据科学
背景情况:
- 尿路生物化学有助于检测和监测复发性结石.
- 目前,没有预测机器学习 (ML) 工具用于结石类型或复发.
- 对结石的预测建模对于个性化患者管理至关重要.
研究的目的:
- 建立和验证ML模型来预测结石类型和复发情况.
- 为了利用24小时的尿液生物化学,年龄,性别和石材成分作为预测特征.
- 评估ML模型在区分石头类型和预测复发方面的有效性.
主要方法:
- 分析了来自三个国际队列 (南安普顿,纽卡斯尔,伯恩) 的数据.
- 使用英国数据,针对石头类型 (5 种模型) 和复发率 (7 种模型) 开发了 ML 模型.
- 模型使用瑞士数据进行外部验证,采用完整案例,多重归因和过量抽样技术.
主要成果:
- 一个极端梯度增强 (XGBoost) 模型有效地区分了氧酸,酸和酸盐石头.
- 这个XGBoost模型在内部和外部验证方面表现出强的表现.
- 没有开发的ML模型能够准确预测石复发.
结论:
- 仅仅24小时的尿路生物化学不足以准确预测石复发.
- 一个单一的ML模型成功地区分了各种结石类型.
- 未来的研究应该纳入放射学和基因组学,以改善石复发的预测工具.
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