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整合常规血液生物标志物和人工智能,以支持工程石材工人的病诊断

Daniel Sanchez-Morillo^1,2, Antonio León-Jiménez^2,3, María Guerrero-Chanivet⁴

¹Department of Engineering on Automation, Electronics and Computer Architecture and Networks University of Cádiz Cádiz Spain.

Bioengineering & translational medicine

|November 15, 2024

概括

工程石病 (ESS) 是一种严重的无治愈的肺部疾病. 这项研究表明,常规血液检查和机器学习可以帮助早期检测ESS,将其与健康个体区分开来.

科学领域:

职业健康职业健康职业健康
肺部医学肺部医学
生物标志物发现发现

背景情况:

工程石病 (ESS) 是一种严重的职业肺部疾病,由吸入粉引起.
由于ESS缺乏有效的治疗方法,并且迅速发展,导致呼吸衰竭和死亡.
通过当前的成像技术,对ESS的早期诊断具有挑战性.

研究的目的:

为了评估常规血液生物标志物的有效性,结合机器学习来区分ESS阶段.
在工程石材工人中识别血中关键标志物,表明有氧化.
探索一种用于早期ESS检测和监测的新型,非侵入性方法.

主要方法:

从107名ESS患者和22名健康对照对21个生化标志物的回顾性分析.
应用Relief-F特征选择技术来识别重要的生物标志物.
使用精选生物标志物的五种机器学习模型的开发和评估.

主要成果:

机器学习模型实现了高性能,灵敏度>82%和特异性>89%.
关键的区分生物标志物包括淋巴细胞百分比,血管酶转化酶和乳酸脱酶.
这些模型成功地区分了健康个体,简单病 (SS) 和渐进性巨型纤维化 (PMF).

结论:

通过机器学习分析的常规血液生物标志物显示出作为ESS诊断的支持工具的潜力.
这些生物标志物可以表明与症相关的慢性炎症.
这种方法可能有助于更早地检测和监测ESS进展.

关键词:

人工石头的人工石头.血液中的生物标志物工程石材是一种工程石材.机器学习是机器学习.石英聚合物聚合物的.在化中,有化.

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