使用具有机器学习的生化制造商预测结核病治疗结果
Zheyue Wang1,2,3, Zhenpeng Guo1,2, Weijia Wang4
1Department of Epidemiology, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical University, Nanjing, 211166, China.
BMC infectious diseases
|February 18, 2025
概括
使用初始患者测试数据,可以预测结核病 (TB) 治疗中止. 高水平的生物标志物,如5'-核酶,尿酸和血球蛋白,表明治疗失败的风险更高.
科学领域:
- 生物医学数据科学是生物医学数据科学.
- 机器学习在医疗保健中的应用
- 传染病建模传染病建模
背景情况:
- 结核病 (TB) 仍然是一个重大的全球卫生挑战.
- 改善患者预后对于降低疾病负担至关重要.
- 目前的预后评估往往侧重于死亡率和耐药性.
研究的目的:
- 通过包括停止治疗来评估结核病预后.
- 扩大预后评估的范围,超越传统的指标.
- 为了识别治疗失败的早期指标.
主要方法:
- 利用了七种特征选择方法和十二种机器学习算法.
- 分析了来自1086名结核病患者的入院测试数据.
- 在特征重要性分析中使用了夏普利添加式解释 (SHAP).
主要成果:
- 具有相互信息的K-近邻分类器实现了0.87.7的AUC.
- 治疗失败的关键预测因素包括高5核酸酶,尿酸,血球蛋白,肌素,囊素C和酸氨酸酶.
- SHAP分析发现5个缩写{'-'}核酶,尿酸和血球蛋白在预测停药方面最具影响力.
结论:
- 开发的模型提供了使用初始患者测试对结核病结果的见解.
- 治疗前生物标志物水平升高与中止治疗的风险增加有关.
- 生物标志物可以作为指导结核病预防和控制策略的早期预警指标.
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