深度学习神经网络导出和测试,以区分急性中毒
Omid Mehrpour1, Christopher Hoyte2, Abdullah Al Masud3
1Michigan Poison & Drug Information Center, Wayne State University School of Medicine, Detroit, MI, USA.
Expert opinion on drug metabolism & toxicology
|July 3, 2023
概括
这项研究开发了深度学习模型,以确定急性中毒的原因. 算法准确地预测了导致中毒的特定药物,有助于诊断.
科学领域:
- 毒理学 毒理学 毒理学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 急性中毒是一个重大的全球健康挑战,通常与未知的致病剂有关.
- 准确识别有毒剂对于有效的患者管理和治疗至关重要.
研究的目的:
- 开发和评估深度学习算法,从预定义的药物清单中预测急性中毒的致病原体.
- 评估PyTorch和Keras深度神经网络在分类单剂中毒中的性能.
主要方法:
- 利用了国家毒素数据系统 (NPDS) 的数据,涵盖了2014-2018年.
- 包括201,031例涉及八种特定药物的单剂中毒病例.
- 应用了两个深度神经网络 (PyTorch和Keras) 来进行多类分类.
主要成果:
- PyTorch模型实现了97%的特异性和83%的准确性,而Keras模型实现了98%的特异性和83%的准确性.
- 两种模型都在识别特定剂方面表现出很高的表现,和硫氨基酸的F1分数高达99%.
- 这些模型在区分八种选择的单剂中毒事件方面表现出强大的能力.
结论:
- 深度神经网络在准确识别急性中毒的致病原体方面表现有前途.
- 开发的模型可以帮助临床医生诊断中毒病例,特别是当暴露剂最初不清楚时.
- 未来的研究可以扩大药物清单,并研究多物质摄入的性能.
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