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Updated: Jun 14, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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开发和时间验证可解释的机器学习模型,用于使用常规实验室分析来预测维生素B12缺乏

Ferhat Demirci1,2, Oktay Yıldırım3, Aylin Demirci4

  • 1Department of Medical Biochemistry, İzmir Tepecik Training and Research Hospital, University of Health Sciences Türkiye, Gaziler Street 468, Yenişehir, Konak, 35120 İzmir, Türkiye.

Diagnostics (Basel, Switzerland)
|February 27, 2026
PubMed
概括

机器学习模型现在可以使用常规实验室测试来预测维生素B12缺乏,从而改善早期检测. 这种方法提高了诊断的准确性,并支持对这种常见缺陷的及时临床干预.

关键词:
在 CatBoost 中使用 CatBoost.临床决策支持 临床决策支持可解释的人工智能实验室诊断 实验室诊断 实验室诊断机器学习是机器学习.维生素B12缺乏症 维生素B12缺乏症

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