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未成熟颗粒细胞和血液生物标志物的作用,在预测穿孔性急性尾炎使用机器学习模型

Zeynep Kucukakcali¹, Sami Akbulut^1,2

¹Department of Biostatistics and Medical Informatics, Inonu University Faculty of Medicine, Malatya 44280, Türkiye.

World journal of clinical cases

|August 7, 2025

概括

一个机器学习模型准确地识别了急性尾炎 (AAp) 和其并发症. 关键的生物标志物,如白细胞计数和总胆红素,有助于诊断,可能改善患者的治疗结果.

科学领域:

医学诊断医学诊断医学诊断
机器学习在医疗保健中的应用.
发现生物标志物的发现.

背景情况:

急性尾炎 (AAp) 是一种常见的需要手术的疾病.
对三分之一的患者来说,区分复杂和无复杂的AAp是一个挑战.
准确的诊断对于及时和适当的治疗至关重要.

研究的目的:

开发一种随机梯度增强 (SGB) 机器学习模型,用于区分复杂和简单的AAp.
通过使用变量重要性分析来确定AAp诊断的重要生物标志物.
为了提高急性尾炎病例的诊断准确度.

主要方法:

对140名个人数据集的分析 (对照组,不复杂的AAp,复杂的AAp).
对人口统计数据和血液生物化学参数的评估.
实施SGB模型与交叉验证和80-20培训测试分割.

主要成果:

该SGB模型在区分AAp与控制器方面实现了96.3%的准确性.
该模型在区分复杂和非复杂的AAp.Ap.中显示了78.9%的准确性.
确定的主要生物标志物包括白细胞 (WBC) 计数,中性粒细胞,淋巴细胞与单细胞的比率和总 bilirubin.

结论:

SGB模型在AAp诊断方面表现出高准确度,在复杂的AAp分类方面表现中等.
已识别的生物标志物对改善诊断过程具有临床意义.
需要进一步的前性研究来验证并将ML算法集成到临床实践中.

关键词:

急性尾炎是什么情况复杂的急性尾炎.机器学习是机器学习.随机梯度的增强促进了随机梯度的增强.

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