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RhDnostics:一种基于机器学习的预测算法模型,用于 RhD 阴性和 DEL 血型查

Meechoke Choodoung¹, Charuporn Promwong², Ketsaraporn Wongba²

¹Department of Mathematics, Faculty of Science, Mahidol University, Bangkok, Thailand.

The journal of applied laboratory medicine

|May 30, 2025

概括

一个机器学习算法准确地识别了RhD阴性血型,有助于选D-elution (DEL) 红细胞. 这种工具可以改善实验室实践和患者安全,在无法进行确认性测试的情况下.

科学领域:

免疫学免疫学免疫学
遗传学遗传学是一个
计算生物学计算生物学

背景情况:

由于红细胞上的低D抗原表达,D-elution (DEL) 现型经常被误认为Rh阴性.
标准的确认方法,如吸附脱离试验和基因定型,在实验室实践中面临挑战,影响患者的安全.
这些挑战包括长时间的交付时间,仪器和试剂的可用性,预算限制和技术困难.

研究的目的:

开发一种用于查D-elution (DEL) 和RhD阴性血型的预测算法.
利用机器学习来准确识别这些血型.

主要方法:

使用 RhCcEe 抗原测试的血清学数据开发了一个机器学习模型.
该模型包含了用于DEL确认的吸附-化试验数据.
该算法是在泰国献血者数据集上进行训练和验证的.

主要成果:

机器学习算法在 RhD 阴性识别方面实现了超过 90% 的预测准确度.
该算法证明了与或没有DEL确认血清学测试的有效性.
一个名为 RhDnostics 的网络应用程序被开发出来,以促进 RhD 阴性查.

结论:

开发的机器学习算法作为实验室RhD阴性查的有价值的预测工具.
当确认血清学测试或RHD分子测试无法获得时,它提供了一个可行的解决方案.
这种方法提高了血液储存中的实验室效率和患者安全.

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