人工智能驱动的算法用于预测COVID-19后急性COVID-19患者对皮质类固醇治疗的反应
Vojtech Myska1, Samuel Genzor2, Anzhelika Mezina1
1Department of Telecommunications, Faculty of Electrical Engineering and Communications, Brno University of Technology, Technicka 12, 616 00 Brno, Czech Republic.
Diagnostics (Basel, Switzerland)
|May 27, 2023
概括
预测COVID-19肺纤维化患者的皮质疗法的益处至关重要. 机器学习模型可以识别可能通过类固醇治疗恢复的个体,帮助COVID-19后肺部并发症的个性化医疗.
科学领域:
- 医学研究 医学研究
- 人工智能在医学中的应用
- 肺部病理学 肺部病理学
背景情况:
- 肺纤维化是COVID-19的一种严重的长期并发症.
- 皮质类固醇治疗可以改善恢复,但存在风险.
- 需要个性化的治疗选择,以优化皮质疗法的结果.
研究的目的:
- 开发和评估机器学习模型,以预测COVID-19后皮质疗法对患者的益处.
- 为管理COVID-19相关的肺纤维化提供个性化治疗决策.
主要方法:
- 使用了多个机器学习算法 (逻辑回归,k-NN,决策树,XGBoost,随机森林,SVM,MLP,AdaBoost,LGBM).
- 在281名患者的数据上训练模型,包括体检,血液检查,肺功能检查和成像 (X射线,HRCT) 在基线和治疗后3个月.
- 使用诸如平衡精度 (BA),ROC-AUC和F1分数等指标评估模型性能.
主要成果:
- 决策树模型表现出强的表现,BA为73.52%,ROC-AUC为74.69%,F1得分为71.70%.
- 随机森林和AdaBoost也显示出具有竞争力的结果,表明了这些算法的潜力.
- 该研究证实,早期COVID-19后的治疗数据可以预测皮质疗法反应.
结论:
- 机器学习模型可以有效地预测哪些COVID-19相关的肺纤维化患者将从皮质疗法中受益.
- 这些预测工具可以帮助临床医生做出个性化治疗决策.
- 早期干预和数据分析是优化COVID-19后护理患者结果的关键.
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