AIMC Topic: Venous Thromboembolism

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Factor Xa inhibitors in clinical practice: Comparison of pharmacokinetic profiles.

Drug metabolism and pharmacokinetics
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...

Multi-Objective Optimization for Personalized Prediction of Venous Thromboembolism in Ovarian Cancer Patients.

IEEE journal of biomedical and health informatics
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...

Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma.

PloS one
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...

Autonomous detection, grading, and reporting of postoperative complications using natural language processing.

Surgery
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...

Prediction of venous thromboembolism using semantic and sentiment analyses of clinical narratives.

Computers in biology and medicine
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
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
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
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
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
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.