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.

This study aims to synthesize the barriers and facilitators of oral nutritional supplement adherence in people with cancer and assess levels of adherence. Databases including Embase, PubMed, Web of Science, Cochrane Library, Chinese National Knowledge Infrastructure, Wanfang Database, and Chinese Scientific Journal Database, were used to search articles before January 4, 2021. Eighteen studies with 1,267 participants met the inclusion criteria. All studies reported rates of adherence, ranging from 6.0% to 96.9%. Gastrointestinal intolerances and disliked tastes were considered common barriers to cancer patients’ adherence, whereas the ONS management strategy was considered the most crucial facilitator of adherence. Health professionals can improve adherence through regular monitoring and guidance and incentive strategies, and paying attention to the management and prevention of adverse reactions when prescribing nutritional products.