Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients

Abstract

Accumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among young-middle-aged inpatients are different from those among elderly people. Therefore, the current prediction models for VTE are not applicable to young-middle-aged inpatients. The aim of this study was to develop and externally validate a new prediction model for young-middle-aged people using machine learning methods. The clinical data sets linked with 167 inpatients with deep venous thrombosis (DVT) and/or pulmonary embolism (PE) and 406 patients without DVT or PE were compared and analysed with machine learning techniques. Five algorithms, including logistic regression, decision tree, feed-forward neural network, support vector machine, and random forest, were used for training and preparing the models. The support vector machine model had the best performance, with AUC values of 0.806–0.944 for 95% CI, 59% sensitivity and 99% specificity, and an accuracy of 87%. Although different top predictors of adverse outcomes appeared in the different models, life-threatening illness, fibrinogen, RBCs, and PT appeared to be more consistently featured by the different models as top predictors of adverse outcomes. Clinical data sets of young and middle-aged inpatients can be used to accurately predict the risk of VTE with a support vector machine model.

Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis

“…The authors in [27] reported an automatic diagnosis model by using effective ML to predict the important risk factors of VTE collecting patient data of the medical ward at King Chulalongkorn Memorial Hospital from Thailand. Other efforts are being dedicated for the prediction of VTE with ML techniques in young and middle-aged inpatients; for example, [28] develop VTE risk classifiers using models based on multi-kernel learning and random optimization [29]. However, a drawback is that these systems are expensive, big, heavy, and have moderate energy consumption.…”

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

“…Some studies have shown that SVM, RF, and XGBoost are the most efficient ML algorithms for classification problems of VTE prediction, respectively. 18 , 19 , 20 Additionally, they are classic and representative algorithms based on different machine learning methods. Therefore, we assumed that the three MLs were effective analytical methods for predicting VTE risk among hospitalized cancer patients.…”

“…16,17 Support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) are the most widely used ML algorithms for VTE prediction. Some studies have shown that SVM, RF, and XGBoost are the most efficient ML algorithms for classification problems of VTE prediction, respectively [18][19][20] . Additionally, they are classic and representative algorithms based on different machine-learning methods.…”

“…Therefore, we assumed that the three MLs were effective analytical methods for predicting VTE risk among hospitalized cancer patients. For comparison, we used logistic regression as a baseline classifier, as in the literature [18][19][20] .…”

Development of predictive models for lymphedema prediction by using blood test and chemotherapy data

“…These methods would require time and high costto diagnose hundreds of patients. Machine learning and data science currently gave great support tomedical science that many predictive models were able to predict disease outcomes with high accuracy [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Machine learning-based detection of lymphedema currently assists doctors and patients in realtime monitoring lymphedema [14,15].…”