Machine learning models for risk prediction of cancer-associated thrombosis: a systematic review and meta-analysis.

Background: Although the number of models for predicting the risk of cancer-associated thrombosis has been rising, there is still a lack of comprehensive assessment for machine learning prediction models.

Objectives: This study aimed to critically appraise and quantify the performance studies using machine learning to predict cancer-associated thrombosis.

Methods: We conducted searches on PubMed, Embase, The Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and other related databases for the related publications (from inception to December 1, 2023).

Establishment and validation of a risk prediction model for sarcopenia in gastrointestinal cancer patients: A systematic review and meta-analysis-based approach.

Objective: The study aimed to develop a model to predict the risk of sarcopenia in gastrointestinal cancer patients. The goal was to identify these patients early and classify them into different risk categories based on their likelihood of developing sarcopenia.

Methods: This study evaluated risk factors for sarcopenia in patients with gastrointestinal cancers through a systematic review and meta-analysis.

Enhancing deep vein thrombosis prediction in patients with coronavirus disease 2019 using improved machine learning model.

Background: Deep vein thrombosis (DVT) is a significant complication in coronavirus disease 2019 patients, arising from coagulation issues in the deep venous system. Among 424 scheduled patients, 202 developed DVT (47.64%).