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从静态值到动态趋势:重新评估血清在解静脉泄漏预测中的血清

Jiang-Peng Zhu¹, Yu-Tong Chen², Guang-Yao Li³

¹Department of Gastrointestinal Surgery, The Second People's Hospital of Wuhu, Wuhu 241000, Anhui Province, China.

World journal of gastrointestinal surgery

|November 3, 2025

概括

一个新的机器学习模型使用血清预测了直肠癌手术后的解静脉泄漏. 通过动态趋势和炎症标志物来增强该模型,可以改善其临床使用.

科学领域:

在瘤学瘤学.
胃肠病学胃肠病学
人工智能在医学中的应用

背景情况:

静脉漏 (AL) 是直肠癌手术后的一种严重并发症.
早期预测AL对于及时干预和改善患者结果至关重要.
机器学习 (ML) 模型为预测复杂的外科手术结果提供了潜力.

研究的目的:

引入一个可解释的ML模型来预测直肠癌手术后的AL.
为了确定AL的关键预测特征,特别是术后血清.
建议改进以提高模型的临床适用性和稳定性.

主要方法:

使用回顾性数据开发可解释的ML模型.
鉴定血清作为一个重要的预测特征.
讨论动态评估的先进建模技术 (时间序列,混合效应模型).

主要成果:

康等人进行的研究. 确定了术后血清作为AL的关键预测因子.
目前的模型依赖于静态门,可能限制其临床效用.
结合动态轨迹和炎症标志物可以提高预测准确度.

结论:

开发的ML模型在预测AL直肠癌后手术方面表现有前途.
提出了动态血清评估和炎症/营养指数整合的建议,以改进模型.
需要进一步验证和明确的临床部署策略,以提高概括性和临床翻译性.

关键词:

解剖学上的泄漏.临床实施临床实施动态监控动态监控动态监控机器学习是机器学习.血清中的.系统性炎症是一种系统性炎症.

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