机器学习算法的比较,以预测有意和无意中毒风险因素
Yousef Veisani1, Hojjat Sayyadi2, Ali Sahebi2
1Psychosocial Injuries Research Center, Ilam University of Medical Sciences, Ilam, Iran.
机器学习准确地预测了中毒因素. 梯度增强树木 (GBT) 模型确定了毒物进入的途径,居住地和精神病史作为故意中毒的关键预测因素.
科学领域:
- 毒理学 毒理学 毒理学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 无论是故意还是非故意的中毒病例,都因地区,年龄和性别而异.
- 了解影响中毒的关键因素对于制定有针对性的预防策略至关重要.
研究的目的:
- 确定导致故意和非故意中毒的最重要因素.
- 评估机器学习算法在预测中毒决定因素方面的有效性.
主要方法:
- 一项涉及658名住院中毒患者的横截面研究.
- 从患者档案和随访 (2020-2021) 中收集的数据使用机器学习算法进行分析.
- 梯度增强树木 (GBT) 模型因其卓越的性能指标 (精度,灵敏度,特异性,AUC) 而被选中.
主要成果:
- GBT模型实现了高精度 (91.5%),灵敏度 (94.7%) 和特异性 (93.2%).
- 蓄意中毒的关键预测因素包括毒物进入的途径 (0.583),居住地 (0.137) 和精神病史 (0.087).
- 无意中毒的重要因素包括年龄 (0.085),二甲暴露,肌素水平和职业.
结论:
- GBT模型是一个可靠的工具,用于识别与故意和无意中毒相关的因素.
- 入境路线,居住地和精神病史对于故意中毒至关重要.
- 年龄,二甲暴露,肌素和职业对于无意中毒有重要意义.
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