BACKGROUND: The anticoagulant actions of oral direct factor Xa (FXa) inhibitors can be inferred from their observed plasma concentrations; however, the steady-state pharmacokinetics (PK) of different FXa inhibitors have not been compared in clinicall...
Using kernel machine learning (ML) and random optimization (RO) techniques, we recently developed a set of venous thromboembolism (VTE) risk predictors, which could be useful to devise a web interface for VTE risk stratification in chemotherapy-treat...
Venous thromboembolism (VTE) is the third most common cardiovascular disorder. It affects people of both genders at ages as young as 20 years. The increased number of VTE cases with a high fatality rate of 25% at first occurrence makes preventive mea...
INTRODUCTION: Natural language processing, a computer science technique that allows interpretation of narrative text, is infrequently used to identify surgical complications. We designed a natural language processing algorithm to identify and grade t...
OBJECTIVE: Machine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether machine learning algorithms can be used to...
IEEE journal of biomedical and health informatics
31562113
Thrombotic events are one of the leading causes of mortality and morbidity related to cancer, with ovarian cancer having one of the highest incidence rates. The need to prevent these events through the prescription of adequate schemes of antithrombot...
BACKGROUND: Postoperative gastrointestinal leak and venous thromboembolism (VTE) are devastating complications of bariatric surgery. The performance of currently available predictive models for these complications remains wanting, while machine learn...
The Khorana score (KS) is used to predict the risk of venous thromboembolism (VTE) for cancer patients. We sought to assess the association between KS and VTE for patients who underwent robot-assisted radical cystectomy (RARC). We reviewed our pros...
Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
33617689
PURPOSE: The aim of the study was to develop and validate machine learning models to predict the personalized risk for 30-day readmission with venous thromboembolism (VTE).