Development and validation of a machine learning model to predict venous thromboembolism among hospitalized cancer patients

作者：Lingqi Meng, Tao Wei, Rongrong Fan, Haoze Su, Jiahui Liu, Lijie Wang, Xinjuan Huang, 一朗 漆崎, Xuying Li · 发表于：Asia-Pacific Journal of Oncology Nursing · 年份：2022 · DOI：10.1016/j.apjon.2022.100128 · 被引用次数：34 · 研究领域：Venous Thromboembolism Diagnosis and Management、Inflammatory Biomarkers in Disease Prognosis、Heparin-Induced Thrombocytopenia and Thrombosis

Objective: Hospitalized cancer patients are at high risk of venous thromboembolism (VTE). However, no predictive model has been specifically developed for this population. Machine learning (ML) is advantageous for model development. This study was aimed at developing predictive models using three different ML algorithms and logistic regression for VTE risk among hospitalized cancer patients and comparing their predictive performance. Methods: A retrospective case-control study was conducted on hospitalized cancer patients at Hunan Cancer Hospital, China, between October 1, 2021, and February 30, 2022. Patients diagnosed with vein thrombosis before or after admission were excluded. Patient, tumor, treatment, and laboratory indicator information was obtained from the hospital information system. The data were randomly split into distributions of 80% for training and 20% for testing. Logistic regression and three ML algorithms-the support vector machine, random forest, and extreme gradient boosting (XGBoost)-were used to develop the models. Model performance was compared using F1, G-mean, area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall rate, and specificity. Feature rankings were achieved based on the permutation scores of the selected features in the optimal model. Results: A total of 1100 patients (mean [SD] age, 54.75 [11.08] years; 485 [44.09%] male) were included in this study. There were 340 patients (30.9%) in the VTE group. The...