在牛皮患者中,与更好的治疗疗效相关的因素:基于决策树模型和后勤回归的研究在中国上海,中国
Fanlingzi Shen1,2, Zhen Duan2, Siyuan Li2
1Clinical Research Center, Shanghai Skin Diseases Hospital, School of Medicine, Tongji University, 1278 Baode Road, Jing'an District, Shanghai, 200443, China.
BMC public health
|July 18, 2024
概括
识别诸如高等教育和避免吸烟等因素可以改善牛皮治疗结果. 这些发现有助于预测牛皮患者的治疗成功.
科学领域:
- 皮肤病学 皮肤病学
- 医疗信息学 医疗信息学
背景情况:
- 尽管包括生物药物在内的牛皮治疗方法取得了进展,但一些患者表现出不理想的治疗反应.
- 确定影响治疗疗效的因素对于优化患者护理至关重要.
研究的目的:
- 探索影响牛皮治疗疗效的因素.
- 为了比较物流回归和决策树模型的预测性能.
主要方法:
- 进行了一项观察性研究,涉及512名牛皮患者.
- 通过问卷和体检收集的数据使用后勤回归和基于CART的决策树模型进行分析.
- 模型性能使用接收机操作员特征 (ROC) 曲线进行评估.
主要成果:
- 47.9%的患者获得了治疗成功 (在第8周的PASI分数有≥75%的改善).
- 高等教育,没有家族病史,避免吸烟和饮酒与更好的治疗成功有关.
- 烟草吸烟成为决策树模型中最关键的预测因素.
- 这两种模型都表现出良好的预测准确性 (AUC ≈ 0.79).
结论:
- 高学历,没有家族病史,不吸烟或饮酒的牛皮患者表现出更好的治疗疗效.
- 决策树模型为预测治疗结果提供了一个实用,直观的替代物流回归方法.
相关概念视频
Survival Tree
78
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
78
Cancer Survival Analysis
336
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
336


