Establishment of machine learning-based tool for early detection of pulmonary embolism

Liu，Lijue1; Li，Yaming2; Liu，Na3; Luo，Jingmin3; Deng，Jinhai4; Peng，Weixiong5; Bai，Yongping3; Zhang，Guogang6; Zhao，Guihu3; Yang，Ning3; Li，Chuanchang3; Long，Xueying3

2024-02-01

10.1016/j.cmpb.2023.107977

Computer methods and programs in biomedicine   影响因子和分区

1872-7565

BACKGROUND AND OBJECTIVES: Pulmonary embolism (PE) is a complex disease with high mortality and morbidity rate, leading to increasing society burden. However, current diagnosis is solely based on symptoms and laboratory data despite its complex pathology, which easily leads to misdiagnosis and missed diagnosis by inexperienced doctors. Especially, CT pulmonary angiography, the gold standard method, is not widely available. In this study, we aim to establish a rapid and accurate screening model for pulmonary embolism using machine learning technology. Importantly, data required for disease prediction are easily accessed, including routine laboratory data and medical record information of patients. METHODS: We extracted features from patients' routine laboratory results and medical records, including blood routine, biochemical group, blood coagulation routine and other test results, as well as symptoms and medical history information. Samples with a feature loss rate greater than 0.8 were deleted from the original database. Data from 4723 cases were retained, 231 of which were positive for pulmonary embolism. 50 features were retained through the positive and negative statistical hypothesis testing which was used to build the predictive model. In order to avoid identification as majority-class samples caused by the imbalance of sample proportion, we used the method of Synthetic Minority Oversampling Technique (SMOTE) to increase the amount of information on minority samples. Five typical machine learning algorithms were used to model the screening of pulmonary embolism, including Support Vector Machines, Logistic Regression, Random Forest, XGBoost, and Back Propagation Neural Networks. To evaluate model performance, sensitivity, specificity and AUC curve were analyzed as the main evaluation indicators. Furthermore, a baseline model was established using the characteristics of the pulmonary embolism guidelines as a comparison model. RESULTS: We found that XGBoost showed better performance compared to other models, with the highest sensitivity and specificity (0.99 and 0.99, respectively). Moreover, it showed significant improvement in performance compared to the baseline model (sensitivity and specificity were 0.76 and 0.76 respectively). More important, our model showed low missed diagnosis rate (0.46) and high AUC value (0.992). Finally, the calculation time of our model is only about 0.05 s to obtain the possibility of pulmonary embolism. CONCLUSIONS: In this study, five machine learning classification models were established to assess the likelihood of patients suffering from pulmonary embolism, and the XGBoost model most significantly improved the precision, sensitivity, and AUC for pulmonary embolism screening. Collectively, we have established an AI-based model to accurately predict pulmonary embolism at early stage.

Data processing Machine learning Pulmonary embolism Xgboost

SCI ; EI

英语

其他

COMPUTER SCIENCE

2-s2.0-85183466094

Scopus

1.School of Automation, Central South University, Changsha, Hunan 410083, China; Xiangjiang Laboratory, Changsha 410205, China; Hunan Zixing Intelligent Medical Technology Co., Ltd, Changsha, Hunan 410000, China
2.School of Automation,Central South University,Changsha,410083,China
3.Xiangya Hospital,Central South University,Changsha,Xiangya Road 87#,410008,China
4.Hunan Zixing Intelligent Medical Technology Co., Ltd, Changsha, Hunan 410000, China; Richard Dimbleby Laboratory of Cancer Research, School of Cancer & Pharmaceutical Sciences, King's College London, London SE1 1UL, UK
5.Hunan Zixing Intelligent Medical Technology Co., Ltd, Changsha, Hunan 410000, China; Department of Electrical and Electronic Engineering, College of Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, Guangdong 518055, China
6.Department of Cardiovascular Medicine,Third Xiangya Hospital,Central South University,Changsha,Tongzipo Road 138#,410008,China

Liu，Lijue,Li，Yaming,Liu，Na,et al. Establishment of machine learning-based tool for early detection of pulmonary embolism[J]. Computer methods and programs in biomedicine,2024,244.

Liu，Lijue.,Li，Yaming.,Liu，Na.,Luo，Jingmin.,Deng，Jinhai.,...&Long，Xueying.(2024).Establishment of machine learning-based tool for early detection of pulmonary embolism.Computer methods and programs in biomedicine,244.

Liu，Lijue,et al."Establishment of machine learning-based tool for early detection of pulmonary embolism".Computer methods and programs in biomedicine 244(2024).