Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support

Key Points Question Can machine-learning approaches achieve an objective pulmonary embolism risk score by analyzing temporal patient data to accurately inform computed tomographic imaging decisions? Findings In this multi-institutional diagnostic study of 3214 patients, a machine learning model was designed to achieve an accurate patient-specific risk score for pulmonary embolism diagnosis. The model was successfully evaluated in both multi-institutional inpatient and outpatient settings. Meaning Machine learning algorithms using retrospective temporal patient data appear to be a valuable and feasible tool for accurate computation of patient-specific risk score to better inform clinical decision-making for computed tomographic pulmonary embolism imaging.