Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations.

Predictive tools for major bleeding (MB) using machine learning (ML) might be advantageous over traditional methods. We used data from the Registro Informatizado de Enfermedad TromboEmbólica (RIETE) to develop ML algorithms to identify patients with venous thromboembolism (VTE) at increased risk of MB during the first 3 months of anticoagulation. A total of 55 baseline variables were used as predictors.

Machine Learning to Predict Outcomes in Patients with Acute Pulmonary Embolism Who Prematurely Discontinued Anticoagulant Therapy.

Background: Patients with pulmonary embolism (PE) who prematurely discontinue anticoagulant therapy (<90 days) are at an increased risk for death or recurrences.

Methods: We used the data from the RIETE (Registro Informatizado de Pacientes con Enfermedad TromboEmbólica) registry to compare the prognostic ability of five machine-learning (ML) models and logistic regression to identify patients at increased risk for the composite of fatal PE or recurrent venous thromboembolism (VTE) 30 days after discontinuation. ML models included decision tree, k-nearest neighbors algorithm, support vector machine, Ensemble, and neural network [NN].

Thirty-day outcomes in patients with proximal deep vein thrombosis who discontinued anticoagulant therapy prematurely.

Introduction: In patients receiving anticoagulation for deep vein thrombosis (DVT), a variety of reasons (including active bleeding or high-risk for bleeding) may lead into premature discontinuation of therapy (prior to completing 90 days). The relative frequency and clinical consequences of premature discontinuation in contemporary patients remain unknown.

Methods: We used the data from RIETE, an international registry of patients with venous thromboembolism (VTE), to identify patients with proximal (above knee) lower limb DVT who prematurely discontinued anticoagulation.