基于机器学习的预测模型,用于多次黄蜂刺伤引起的急性损伤
Wen Wu1, Yupei Zhang1, Yilan Zhang1
1Department of Emergency Medicine, Yichang Central People's Hospital, Yichang, 443003, Hubei, China; Department of Critical Care Medicine, Yichang Central People's Hospital, Yichang, 443003, Hubei, China; The First College of Clinical Medical Science, China Three Gorges University, Yichang, 443003, Hubei, China.
Toxicon : official journal of the International Society on Toxinology
|September 30, 2024
概括
一个新的模型预测了黄蜂后的急性损伤 (AKI). 它使用刺痛数,血,SIRI和血小板计数来早期识别高风险患者,以便更好地管理.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 毒理学 毒理学 毒理学
- 机器学习在医学中的应用
背景情况:
- 急性损伤 (AKI) 是多次黄蜂的严重并发症.
- 目前,与黄蜂刺相关的AKI的预测模型有限.
- 早期识别有风险的个体对于及时干预至关重要.
研究的目的:
- 开发和验证基于机器学习的临床预测模型,用于在患有黄蜂的患者中对AKI进行预测.
- 为了确定与黄蜂中毒后的AKI发展相关的关键预后变量.
- 为了创建一个用户友好的临床应用的nomogram.
主要方法:
- 对214名患有黄蜂刺伤的患者进行了回顾性队列研究.
- 使用最小绝对收缩和选择运算符 (LASSO) 和多变量后勤回归来确定预后因素.
- 使用内部验证,交叉验证,NRI,IDI和DCA构建和验证了一个名录.
主要成果:
- 34.6%的患者发生了AKI.
- 确定了关键预测因素:刺伤数量,大血,SIRI和血小板计数.
- 诺米图表显示出良好的预测准确性 (AUC:0.757) 和歧视性.
结论:
- 开发的野图是预测黄蜂患者AKI风险的可靠工具.
- 这种模式有助于早期风险分层和风险管理.
- 在不同人群中进一步验证可能会提高其通用性.
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