首页|Huazhong University of Science and Technology Reports Findings in Pulmonary Embo lism (Machine-learning-based models assist the prediction of pulmonary embolism in autoimmune diseases: A retrospective, multicenter study)
Huazhong University of Science and Technology Reports Findings in Pulmonary Embo lism (Machine-learning-based models assist the prediction of pulmonary embolism in autoimmune diseases: A retrospective, multicenter study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Lung Diseases and Cond itions - Pulmonary Embolism is the subject of a report. According to news report ing out of Hubei, People's Republic of China, by NewsRx editors, research stated , "Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with hi gh mortality among patients with autoimmune inflammatory rheumatic diseases (AII RDs). Accurate prediction and timely intervention play a pivotal role in enhanci ng survival rates." Our news journalists obtained a quote from the research from the Huazhong Univer sity of Science and Technology, "However, there is a notable scarcity of practic al early prediction and risk assessment systems of PE in patients with AIIRD. In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease- matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospi tal from 2014 to 2022. Univariable logistic regression (LR) and least absolute s hrinkage and selection operator (LASSO) were used to select the clinical feature s for further training with machine learning (ML) methods,including random fore st (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression tree s (CART), and C5.0 models. The performances of these models were subsequently va lidated using a multicenter validation cohort. In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respect ively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performan ces, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the trai ning cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achi eved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer level s alone."
HubeiPeople's Republic of ChinaAsiaAutoimmune Diseases and ConditionsAutoimmunityCardiovascular Diseases and C onditionsCyborgsEmbolismEmbolism and ThrombosisEmerging TechnologiesHe alth and MedicineImmune System Diseases and ConditionsImmunologyLung Disea ses and ConditionsMachine LearningPulmonary EmbolismRespiratory Tract Dise ases and ConditionsVascular Diseases and Conditions
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