AI-Powered Pipeline for Annotating Echocardiography Notes and Prognostic Variable Analysis in Critical Care

BackgroundEchocardiography (echo) notes contain valuable prognostic information for patients in the intensive care unit (ICU). However, their unstructured format and the presence of sensitive patient information present challenges for large-scale, automated analysis. There is a need for secure and efficient methods to extract and utilize echo data to enhance ICU outcome prediction. MethodsWe developed an AI-powered, privacy-preserving pipeline that leverages advanced natural language processing and pattern matching to annotate echo notes locally, ensuring comprehensive masking of personally identifiable information. This pipeline was applied to patient data from a mixed medical surgical ICU in a tertiary referral hospital. Key variables were extracted from echo notes and integrated with clinical and laboratory data to predict ICU mortality. A LightGBM machine learning model--robust to missing values--was trained using both routine and echo-derived structured clinical features. Its predictive performance was compared to that of the APACHE IV score. ResultsCompared with the reference standard derived from manual annotation by echocardiography specialist, automated annotation of echo notes achieved 98.85% data accuracy with a false positive rate of 0.31%. Several echo-derived variables, including left ventricular ejection fraction (LVEF), left ventricular outflow tract velocity time integral (LVOT VTI), tricuspid annular plane systolic excursion (TAPSE), mitral regurgitation (MR), and aortic regurgitation (AR), were strongly associated with ICU mortality. Incorporating echo-derived variables improved the accuracy in prediction of ICU mortality, with the LightGBM model achieving an AUC of 0.902 compared to 0.861 for APACHE IV score. ConclusionOur locally deployable AI pipeline enables secure and automated extraction of prognostic information from echo notes, substantially enhancing ICU mortality prediction. The inclusion of echo-derived variables significantly improved predictive accuracy, underscoring the potential but currently underutilized value of unstructured notes. This approach paves the way for scalable, privacy-preserving decision support tools in critical care.