@文章{信息:doi/10.2196/17784，作者="Obeid, Jihad S和Dahne, Jennifer和Christensen, Sean和Howard, Samuel和Crawford, Tami和Frey, Lewis J和Stecker, Tracy和Bunnell, Brian E"，标题="识别和预测电子健康记录临床笔记中的故意自残:深度学习方法"，期刊="JMIR Med Inform"，年="2020"，月="7月"，日="30"，卷="8"，数="7"，页数="e17784"，关键词="机器学习;深度学习;自杀;自杀未遂;电子健康记录;背景:自杀在美国和世界各地都是一个重要的公共卫生问题。利用现有的数据集，研究机器学习方法来识别和预测故意自残和自杀的工作已经有了重大进展。随着最近计算技术的进步，深度学习在医疗保健领域的应用正获得发展势头。目的:本研究旨在利用深度神经网络(DNNs)利用临床记录中的信息(1)提高对接受故意自残治疗的患者的识别能力(2)预测未来的自残事件。方法:我们从835例具有国际疾病分类(ICD)故意自残代码的患者的电子健康记录(EHRs)和1670例从未有任何故意自残ICD代码的匹配对照中提取临床文本记录。 The data were divided into training and holdout test sets. We tested a number of algorithms on clinical notes associated with the intentional self-harm codes using the training set, including several traditional bag-of-words--based models and 2 DNN models: a convolutional neural network (CNN) and a long short-term memory model. We also evaluated the predictive performance of the DNNs on a subset of patients who had clinical notes 1 to 6 months before the first intentional self-harm event. Finally, we evaluated the impact of a pretrained model using Word2vec (W2V) on performance. Results: The area under the receiver operating characteristic curve (AUC) for the CNN on the phenotyping task, that is, the detection of intentional self-harm in clinical notes concurrent with the events was 0.999, with an F1 score of 0.985. In the predictive task, the CNN achieved the highest performance with an AUC of 0.882 and an F1 score of 0.769. Although pretraining with W2V shortened the DNN training time, it did not improve performance. Conclusions: The strong performance on the first task, namely, phenotyping based on clinical notes, suggests that such models could be used effectively for surveillance of intentional self-harm in clinical text in an EHR. The modest performance on the predictive task notwithstanding, the results using DNN models on clinical text alone are competitive with other reports in the literature using risk factors from structured EHR data. ", issn="2291-9694", doi="10.2196/17784", url="https://medinform.www.mybigtv.com/2020/7/e17784", url="https://doi.org/10.2196/17784", url="http://www.ncbi.nlm.nih.gov/pubmed/32729840" }