AIMC Topic: Venous Thromboembolism
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Prediction of venous thromboembolism using semantic and sentiment analyses of clinical narratives.
Computers in biology and medicine
Jan 3, 2018
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...
Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology.
Disease markers
Sep 17, 2017
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...
Creation of a simple natural language processing tool to support an imaging utilization quality dashboard.
International journal of medical informatics
Feb 21, 2017
BACKGROUND: Testing for venous thromboembolism (VTE) is associated with cost and risk to patients (e.g. radiation). To assess the appropriateness of imaging utilization at the provider level, it is important to know that provider's diagnostic yield (...
The safety and efficacy of full- versus reduced-dose betrixaban in the Acute Medically Ill VTE (Venous Thromboembolism) Prevention With Extended-Duration Betrixaban (APEX) trial.
American heart journal
Dec 18, 2016
BACKGROUND: The APEX trial assessed the safety and efficacy of extended-duration thromboprophylaxis using betrixaban versus standard dosing of enoxaparin among hospitalized, acutely ill medical patients. The 80-mg betrixaban dose was halved to 40 mg ...
Risk Assessment for Venous Thromboembolism in Chemotherapy-Treated Ambulatory Cancer Patients.
Medical decision making : an international journal of the Society for Medical Decision Making
Aug 19, 2016
OBJECTIVE: To design a precision medicine approach aimed at exploiting significant patterns in data, in order to produce venous thromboembolism (VTE) risk predictors for cancer outpatients that might be of advantage over the currently recommended mod...
Comparison of bleeding risks related to venous thromboembolism prophylaxis in laparoscopic vs open colorectal cancer surgery: a multicenter study in Japanese patients.
American journal of surgery
Dec 13, 2015
BACKGROUND: Venous thromboembolism is the most common preventable cause of hospital death. The objective of this study was to clarify risk factors for postoperative bleeding related to thromboprophylaxis after laparoscopic colorectal cancer surgery.
Risks and Benefits of Pharmacological Prophylaxis for Venous Thromboembolism Prevention in Patients Undergoing Robotic Partial Nephrectomy.
The Journal of urology
Nov 26, 2015
PURPOSE: We investigate the safety and efficacy of pharmacological venous thromboembolism prophylaxis in patients treated with robotic partial nephrectomy at our center.
Non-weight-based enoxaparin dosing subtherapeutic in trauma patients.
The Journal of surgical research
Oct 21, 2015
BACKGROUND: We report our experience dosing and monitoring enoxaparin with anti-factor Xa activity (anti-FXaA) levels for venous thromboembolism prophylaxis in trauma patients (TP).
A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data.
Journal of the American Medical Informatics Association : JAMIA
Oct 20, 2014
BACKGROUND: Venous thromboembolisms (VTEs), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), are associated with significant mortality, morbidity, and cost in hospitalized patients. To evaluate the success of preventive measures,...
SSA-sMLP: A venous thromboembolism risk prediction model using separable self-attention and spatial-shift multilayer perceptrons.
Thrombosis research
Jun 1, 2025
Accurate risk assessment of Venous Thromboembolism (VTE) holds significant value for clinical decision-making. However, traditional scoring systems relying on linear assumptions and expert experience, along with machine learning models constrained by...
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