@Article{info:doi/10.2196/30720，作者=“Wang, Ni and Wang, Muyu and Zhou, Yang and Liu, Honglei and Wei, Lan and Fei, Xiaolu and Chen, Hui”，标题=“基于顺序数据的患者相似度框架用于患者预后预测:算法开发”，期刊=“J Med Internet Res”，年=“2022”，月=“Jan”，日=“6”，卷=“24”，号=“1”，页=“e30720”，关键词=“患者相似度”;电子病历;时间序列;急性心肌梗死;自然语言处理;机器学习;深度学习;结果预测;信息学;背景:电子病历中的顺序信息对患者预后预测很有价值，但由于其不均匀性、不规则性和异质性，很少用于患者相似性测量。 Objective: We aimed to develop a patient similarity framework for patient outcome prediction that makes use of sequential and cross-sectional information in electronic medical record systems. Methods: Sequence similarity was calculated from timestamped event sequences using edit distance, and trend similarity was calculated from time series using dynamic time warping and Haar decomposition. We also extracted cross-sectional information, namely, demographic, laboratory test, and radiological report data, for additional similarity calculations. We validated the effectiveness of the framework by constructing k--nearest neighbors classifiers to predict mortality and readmission for acute myocardial infarction patients, using data from (1) a public data set and (2) a private data set, at 3 time points---at admission, on Day 7, and at discharge---to provide early warning patient outcomes. We also constructed state-of-the-art Euclidean-distance k--nearest neighbor, logistic regression, random forest, long short-term memory network, and recurrent neural network models, which were used for comparison. Results: With all available information during a hospitalization episode, predictive models using the similarity model outperformed baseline models based on both public and private data sets. For mortality predictions, all models except for the logistic regression model showed improved performances over time. There were no such increasing trends in predictive performances for readmission predictions. The random forest and logistic regression models performed best for mortality and readmission predictions, respectively, when using information from the first week after admission. Conclusions: For patient outcome predictions, the patient similarity framework facilitated sequential similarity calculations for uneven electronic medical record data and helped improve predictive performance. ", issn="1438-8871", doi="10.2196/30720", url="//www.mybigtv.com/2022/1/e30720", url="https://doi.org/10.2196/30720", url="http://www.ncbi.nlm.nih.gov/pubmed/34989682" }