加强深度学习模型来预测吸烟状态,使用慢性阻塞性肺病患者的临床数据
Sehyun Cho1, Hyeonseok Jin2, Kyungbaek Kim2
1College of Nursing, Chonnam National University, Gwangju, Republic of Korea.
深度学习模型通过整合行为和临床数据,准确地预测慢性阻塞性肺病 (COPD) 患者的持续吸烟. 关键预测因素包括戒烟建议,就业和压力水平,有助于有针对性的戒烟策略.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 肺部病理学 肺部病理学
背景情况:
- 慢性阻塞性肺病 (COPD) 是一个主要的健康问题,经常因持续吸烟而复杂化.
- 预测COPD患者的吸烟持续性对于有效干预至关重要.
- 当前的预测模型可能无法完全捕捉到这一群体中吸烟行为的复杂性.
研究的目的:
- 开发和评估深度学习模型,以更好地预测COPD患者持续吸烟的情况.
- 整合行为,心理和临床数据,以提高预测准确度.
- 为了确定持续吸烟的关键预测因子,针对目标戒烟干预措施.
主要方法:
- 开发并评估了三个深度学习模型和一个机器学习模型.
- 利用了350名COPD患者的临床,行为和心理社会数据.
- 采用数据预处理,超参数优化 (Optuna) 和交叉验证;使用SHAP进行解释性.
主要成果:
- 一个残余神经网络实现了最高的性能,宏观F1得分为0.87.
- 发现的关键预测因素包括专业的戒烟建议,就业状况,唾液症状,感知到的压力,健康检查和健康素养.
- 沙普利添加式解释 (SHAPs) 提供了关于特征重要性的见解.
结论:
- 整合行为和心理社会数据显著改善了COPD持续吸烟的预测.
- 多维数据有助于识别戒烟的高风险人群.
- 研究结果支持制定针对性戒烟策略,以适应COPD患者的需求.
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