Network analysis of unstructured EHR data for clinical research.
In biomedical research, network analysis provides a conceptual framework for interpreting data from high-throughput experiments. For example, protein-protein interaction networks have been successfully used to identify candidate disease genes. Recently, advances in clinical text processing and the increasing availability of clinical data have enabled analogous analyses on data from electronic medical records. We constructed networks of diseases, drugs, medical devices and procedures using concepts recognized in clinical notes from the Stanford clinical data warehouse. We demonstrate the use of the resulting networks for clinical research informatics in two ways-cohort construction and outcomes analysis-by examining the safety of cilostazol in peripheral artery disease patients as a use case. We show that the network-based approaches can be used for constructing patient cohorts as well as for analyzing differences in outcomes by comparing with standard methods, and discuss the advantages offered by network-based approaches.