@Article{info:doi/10.2196/37486，作者=“黄彦群与郑，志敏与马，莫轩与欣，欣与刘，洪磊与费，小璐与魏，兰与陈辉”，标题=“基于电子病历嵌入表征的急性心肌梗死住院患者预后预测性能的提高:《发展与验证研究》，期刊=“J Med Internet Res”，年=“2022”，月=“8”，日=“3”，卷=“24”，号=“8”，页=“e37486”，关键词=“表征学习”;skip-gram;特征关联强度;功能的重要性;死亡风险预测;背景:电子病历(EMRs)的广泛二次使用促进了医疗保健质量的提高。表征学习能够自动地从EMR数据中提取隐藏信息，已受到越来越多的关注。目的:我们旨在提出具有更多特征关联和任务特异性特征重要性的患者表征，以提高急性心肌梗死(AMI)住院患者的预后预测性能。方法:医学概念，包括患者的年龄、性别、疾病诊断、实验室检查、结构化放射特征、程序和药物，首先使用改进的skip-gram算法嵌入到实值向量中，其中上下文窗口中的概念通过关联规则置信度测量的特征关联强度来选择。 Then, each patient was represented as the sum of the feature embeddings weighted by the task-specific feature importance, which was applied to facilitate predictive model prediction from global and local perspectives. We finally applied the proposed patient representation into mortality risk prediction for 3010 and 1671 AMI inpatients from a public data set and a private data set, respectively, and compared it with several reference representation methods in terms of the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and F1-score. Results: Compared with the reference methods, the proposed embedding-based representation showed consistently superior predictive performance on the 2 data sets, achieving mean AUROCs of 0.878 and 0.973, AUPRCs of 0.220 and 0.505, and F1-scores of 0.376 and 0.674 for the public and private data sets, respectively, while the greatest AUROCs, AUPRCs, and F1-scores among the reference methods were 0.847 and 0.939, 0.196 and 0.283, and 0.344 and 0.361 for the public and private data sets, respectively. Feature importance integrated in patient representation reflected features that were also critical in prediction tasks and clinical practice. Conclusions: The introduction of feature associations and feature importance facilitated an effective patient representation and contributed to prediction performance improvement and model interpretation. ", issn="1438-8871", doi="10.2196/37486", url="//www.mybigtv.com/2022/8/e37486", url="https://doi.org/10.2196/37486", url="http://www.ncbi.nlm.nih.gov/pubmed/35921141" }