Inferring Clinical Correlations from EEG Reports with Deep Neural Learning

Successful diagnosis and management of neurological dysfunction relies on proper communication between the neurologist and the primary physician (or other specialists). Because this communication is documented within medical records, the ability to automatically infer the clinical correlations for a patient from his or her medical records would provide an important step towards enabling health care systems to automatically identify patients requiring additional follow-up as well as flagging any unexpected clinical correlations for review. In this paper, we present a Deep Section Recovery Model (DSRM) which applies deep neural learning on a large body of EEG reports in order to infer the expected clinical correlations for a patient from the information in a given EEG report by (1) automatically extracting word- and report- level features from the report and (2) inferring the most likely clinical correlations and expressing those clinical correlations in natural language. We evaluated the performance of the DSRM by removing the clinical correlation sections from EEG reports and measuring how well the model could recover that information from the remainder of the report. The DSRM obtained a 17% improvement over the top-performing baseline, highlighting not only the power of the DSRM but also the promise of automatically recognizing unexpected clinical correlations in the future.