IDENTIFYING IMPLIED CRITERIA IN CLINICAL TRIALS USING MACHINE LEARNING TECHNIQUES

A method and apparatus for identifying implied criteria for a clinical trial is disclosed. An example method generally includes generating a training data set from a corpus of clinical trial specifications. The training data set may include at least a first sample corresponding to a first trial. The first sample may include a first feature based on one or more explicitly stated trial criteria, a second feature based on metadata describing the first trial, and a third feature based on patient data of patients associated with the first trial. A machine learning model is trained, using a supervised learning approach, based on the training data set. A system processes a second trial as an input to the trained machine learning model to determine one or more implied criteria that are not explicitly enumerated in a specification for the second trial.