Machine learning with D-dimer in the risk stratification for pulmonary embolism: a derivation and internal validation study.

Aim: To develop a machine learning model to predict the diagnosis of pulmonary embolism (PE).

Methods And Results: We undertook a derivation and internal validation study to develop a risk prediction model for use in patients being investigated for possible PE. The machine learning technique, generalized logistic regression using elastic net, was chosen following an assessment of seven machine learning techniques and on the basis that it optimized the area under the receiver operator characteristic curve (AUC) and Brier score.