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Medication-based mortality prediction in COPD using machine learning and conventional statistical methods
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DOI：10.1016/j.ijmedinf.2025.106177.png)
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摘要
En 中文
• 预测工具通常需要常规就诊期间无法电子化获取的数据。
• 药品索赔数据支持在常规就诊期间进行实时风险分层。
• 深度神经网络模型略优于逻辑回归模型。
• 包含合并症用药信息显著提高了模型性能。
• SHAP分析突出了非吸入性抗胆碱能药物和利尿剂等主要预测因子。
Keyword:
COPD management
Medication adherence
Machine learning
Deep neural network
Prediction model
AUC-ROC
Area under the receiver operating characteristic curve
AUC-PR
Area under the precision-recall curve
CE
Cohort entry
CI
Confidence interval
COPD
Chronic obstructive pulmonary disease
D-ANN
Deep artificial neural network
ICS
Inhaled corticosteroid
LAAC
Long-acting anticholinergic
LABA
Long-acting β2-agonist
LASSO
Least absolute shrinkage and selection operator
LTRA
Leukotriene-receptor antagonist
METH
Methylxanthines
ML
Machine learning
OCS
Oral corticosteroid
PDC
Proportion of days covered
RA
Respiratory antibiotics
RAMQ
Régie de l’assurance maladie du Québec
RF
Random forest
SAAC
Short-acting anticholinergic
SABA
Short-acting β2-agonist
SHAP
SHapley Additive exPlanations
S-ANN
Simple artificial neural network
XGBoost
Extreme Gradient Boosting
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.1
论文数:
4.6K
被引数:
1.1W
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引用论文
Henri S, Herrera R, Vanasse A, Forget A, Blais L. Trajectories of care in patients with chronic obstructive pulmonary disease: A sequence analysis. Can J Respir Crit Care Sleep Med 2022;6:237–47. DOI: 10.1080/24745332.2021.1978907.Henri S，Herrera R，Vanasse A，Forget A，Blais L. 慢性阻塞性肺疾病患者的照护轨迹：一项序列分析。Can J Respir Crit Care Sleep Med 2022;6:237–47. DOI: 10.1080/24745332.2021.1978907..png)
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