AIMC Topic: Venous Thromboembolism
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Evaluation of risk factors for thromboembolic events in multiple myeloma patients using multiple machine learning models.
Medicine
Feb 14, 2025
Venous thromboembolic events (VTE) is a frequent complication in multiple myeloma (MM) patients, raising mortality. This study aims to use machine learning to identify VTE risk factors in MM, helping to pinpoint high-risk individuals for better clini...
Deep Learning-Based Dynamic Risk Prediction of Venous Thromboembolism for Patients With Ovarian Cancer in Real-World Settings From Electronic Health Records.
JCO clinical cancer informatics
Jul 1, 2024
PURPOSE: Patients with epithelial ovarian cancer (EOC) have an elevated risk for venous thromboembolism (VTE). To assess the risk of VTE, models were developed by statistical or machine learning algorithms. However, few models have accommodated deep ...
Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.
Blood advances
Jun 25, 2024
Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual medical record review and diagnosis code interpretation. Natural language processing (NLP) can automate ...
Extended Venous Thromboembolism Prophylaxis after Robotic Staging for Endometrial Cancer.
Southern medical journal
Oct 1, 2023
OBJECTIVES: Our objectives were to estimate the incidence of venous thromboembolism (VTE) after robotic staging for endometrial cancer and to compare the incidence of VTE in patients who received a single dose of preoperative prophylaxis of enoxapari...
Low Risk of Venous Thromboembolism After Robot-assisted Radical Prostatectomy Through Systemic Image Assessment: A Prospective Study.
In vivo (Athens, Greece)
Jan 1, 2022
BACKGROUND/AIM: The aim of the study was to evaluate the risk of venous thromboembolism (VTE) after robot-assisted radical prostatectomy (RARP) and discuss whether a uniform prophylaxis for VTE after radical prostatectomy is also suitable for robotic...
Prediction and Diagnosis of Venous Thromboembolism Using Artificial Intelligence Approaches: A Systematic Review and Meta-Analysis.
Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
Jan 1, 2021
Venous thromboembolism (VTE) is a fatal disease and has become a burden on the global health system. Recent studies have suggested that artificial intelligence (AI) could be used to make a diagnosis and predict venous thrombosis more accurately. Thus...
Natural Language Processing Performance for the Identification of Venous Thromboembolism in an Integrated Healthcare System.
Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
Jan 1, 2021
Real-time identification of venous thromboembolism (VTE), defined as deep vein thrombosis (DVT) and pulmonary embolism (PE), can inform a healthcare organization's understanding of these events and be used to improve care. In a former publication, we...
Predicting Surgical Complications in Patients Undergoing Elective Adult Spinal Deformity Procedures Using Machine Learning.
Spine deformity
Jan 1, 2018
STUDY DESIGN: Cross-sectional database study.
The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children.
Journal of thrombosis and thrombolysis
Oct 1, 2017
Venous thromboembolism (VTE) is a potentially life-threatening condition that includes both deep vein thrombosis (DVT) and pulmonary embolism. We sought to improve detection and reporting of children with a new diagnosis of VTE by applying natural la...
Automated Extraction of VTE Events From Narrative Radiology Reports in Electronic Health Records: A Validation Study.
Medical care
Oct 1, 2017
BACKGROUND: Surveillance of venous thromboembolisms (VTEs) is necessary for improving patient safety in acute care hospitals, but current detection methods are inaccurate and inefficient. With the growing availability of clinical narratives in an ele...
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