不平衡的机器学习分类模型,用于移除生物类似药物和在患有风湿性疾病的患者中增加活性
David Castro Corredor1, Luis Ángel Calvo Pascual2
1Rheumatology Department, Hospital General Universitario de Ciudad Real, Ciudad Real, Spain.
PloS one
|November 30, 2023
概括
机器学习模型可以预测患者的风湿性疾病恶化和生物类似药物清除. 关键因素包括年龄和切换史,有助于治疗决策.
科学领域:
- 类风湿病学 类风湿病学
- 药理学 药理学是指药理学的学科.
- 数据科学数据科学数据科学
背景情况:
- 风湿性疾病需要长期管理.
- 生物类似药物越来越多地使用.
- 预测治疗反应和药物存活率至关重要.
研究的目的:
- 预测长期疾病恶化在类风湿性疾病.
- 为了预测生物类似药物的清除.
- 为这些预测开发机器学习模型.
主要方法:
- 对患有免疫媒介性炎症性类风湿病的患者进行了回顾性观察性研究.
- 对转换为生物类似药物的患者的分析.
- 使用不平衡的机器学习模型 (决策树),根据f1得分和平均ROC AUC选择.
主要成果:
- 开发了决策树模型,f1得分为0.52用于疾病恶化和0.63用于生物类似物清除.
- 确定了关键预测因素:年龄和生物类似药切换恶化;优化和初始PCR删除.
- 强调了预测疾病恶化等罕见事件的挑战.
结论:
- 不平衡的机器学习模型可以预测疾病恶化和类风湿性疾病中的生物类似物清除.
- 年龄,切换和优化等因素成为重要预测因素.
- 这些模型可以帮助识别患有不良结果或药物停用风险的患者.
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