@文章{信息:doi/10.2196/21604，作者=“李道伟和张，强和谭，岳和冯，星霍和岳，袁一和白，玉涵和李，吉萌和李，嘉航和徐，陈友军，石玉和肖，思玉和孙，木燕和李，晓娜和朱，芳”，标题=“利用胸部ct和实验室测量预测COVID-19严重程度:，期刊="JMIR Med Inform"，年="2020"，月="11月"，日="17"，量="8"，数="11"，页数="e21604"，关键词="COVID-19;重症病例预测;电脑断层摄影术;机器学习;CT;扫描;检测;预测;背景:COVID-19导致的大多数死亡都与严重疾病有关。 Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. Objective: This study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. Methods: A total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients' CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. Results: We present a prediction model combining patients' radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F1 score of 0.89, which showed a 6{\%} and 15{\%} improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients' laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. Conclusions: To our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images. ", issn="2291-9694", doi="10.2196/21604", url="http://medinform.www.mybigtv.com/2020/11/e21604/", url="https://doi.org/10.2196/21604", url="http://www.ncbi.nlm.nih.gov/pubmed/33038076" }