step of the model was defining the relationships between entities. After all previous steps are completed, and the model knows what the tagged entities (measures, organs, locations) are referring to, as well as knows the syntactic dependencies between them, the model uses a set of rules and heuristics to determine semantic relations between entities. • Once the relations extraction is completed, the model has all the required information to extract data of interest, in this case, a set of measured lung nodules. It excludes measurements from reference citations (such as the Fleischner Society guidelines for incidentally detected lung nodule follow-up). Finally, the model selects the largest nodule and outputs its size, location, and characteristics. • We found the accuracy agreement between the model and the annotated gold standard records for the presence of a lung nodule was 98.95%, with 98.99% precision and 99.66% recall. • The model is a brain of our healthcare product that is currently being used in hundreds of hospitals in US, impacting thousands of patients every year and saving crucial lives.