无监督特征选择以识别冠状动脉疾病患者队列中机器学习的重要ICD-10和ATC代码:回顾性研究
Peyman Ghasemi1,2, Joon Lee1,3,4,5
1Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
JMIR medical informatics
|August 1, 2024
概括
混凝土自动编码器有效地从复杂的健康数据中选择关键特征,改进机器学习模型,用于预测患者的死亡率等结果. 这种方法增强了数据表示和模型可解释性.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 医疗保健中的机器学习使用国际疾病分类 (ICD) 和解剖治疗化学 (ATC) 等等级代码.
- 无监督的特征选择对于管理高维的健康数据,减少复杂性和防止过度拟合至关重要.
- 由于ICD和ATC代码的庞大数量和层次性质,现有的方法面临着挑战.
研究的目的:
- 为了比较ICD和ATC代码数据库的无监督特征选择方法.
- 为了确定代表冠状动脉疾病患者的最佳特征集.
- 为了评估所选特征的性能,复杂性和可解释性.
主要方法:
- 拉普拉斯分数,UFS,自编码器启发的方法,PCA和混凝土自编码器 (有/没有重量调整) 的比较.
- 利用了2个ICD和1个ATC代码数据库,来自51,506名冠状动脉疾病患者.
- 评估特征重建准确性,90天死亡率预测,特征复杂性 (代码级),以及可解释性 (沙普利分析).
主要成果:
- 混凝土自编码方法在特征重建和死亡率预测方面表现优于其他方法.
- 重量调整混凝土自动编码器显示出优异的重建精度和预测性能 (P<.05).
- 混凝土自动编码器偏好了一般代码,并为死亡率预测提供了更高的Shapley值.
结论:
- 混凝土自动编码器在不受监督的情况下从ICD和ATC数据中选择突出的特征时优越.
- 对于混凝土自动编码器而言,一种新的重量调整方法提高了健康码数据的概括性和解释性.
- 这种方法为未来的医疗保健机器学习研究提供了宝贵的资产.
关键词:
在ATC中,ATC是ATC.解剖学 治疗 化学 化学在ICD-10中,它被列为ICD-10.疾病的国际分类.疾病国际分类,第十次修订.拉普拉西亚人的得分.动脉 动脉 动脉 动脉 动脉动脉疾病是一种动脉疾病.人工智能的人工智能是人工智能.自动编码器启发的无监督功能选择.一个案例研究研究.一个混凝土自动编码器.冠状动脉疾病是一种冠状动脉疾病.数据集数据集数据集.可以解释的解释性.机器学习是机器学习.死亡率 死亡率死亡率预测死亡率预测患者队列队列患者队列主要特征分析的主要特征分析无监督的特征选择选择.对于多集群数据的无监督特征选择.更多相关视频
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