Clinical Application of Machine Learning in the Assessment of Pulmonary Embolism in Patients with Gastrointestinal Cancer

Abstract Pulmonary thromboembolism (PTE) is one of the most important complications in gastrointestinal cancer patients. However, there were few studies that predict pulmonary embolism using machine learning (ML). The purpose of this study was to develop an ML based prediction model for PTE in gastrointestinal cancer patients, and to compare its performance with the conventional model. In a tertiary hospital, patients who underwent computed tomographic pulmonary angiography (CTPA) were reviewed retrospectively from 2010 to 2020. Demographic and predictor variables including the Wells score and D-dimer were investigated. A total of 446 gastrointestinal cancer patients were analyzed in this study. The overall incidence of PTE was 30.0%. Compared with the conventional model (AUROC 0.605), the performance of ML model predicting PTE was improved (0.706, P = 0.002) and was further improved with additional input of further demographic factors including age and sex (0.743, P < 0.001). The number of patients classified as requiring CTPA was significantly reduced according to the prediction with ML (1.8% vs 9.4%, P < 0.001). Prediction model based on ML might have advantages to improve the diagnostic performance and reduce the number of CTPA compared to the conventional model for PTE in patients with gastrointestinal cancer.