title: Building the graph of medicine from millions of clinical narratives.
abstract: Electronic health records (EHR) represent a rich and relatively untapped resource for characterizing the true nature of clinical practice and for quantifying the degree of inter-relatedness of medical entities such as drugs, diseases, procedures and devices. We provide a unique set of co-occurrence matrices, quantifying the pairwise mentions of 3 million terms mapped onto 1 million clinical concepts, calculated from the raw text of 20 million clinical notes spanning 19 years of data. Co-frequencies were computed by means of a parallelized annotation, hashing, and counting pipeline that was applied over clinical notes from Stanford Hospitals and Clinics. The co-occurrence matrix quantifies the relatedness among medical concepts which can serve as the basis for many statistical tests, and can be used to directly compute Bayesian conditional probabilities, association rules, as well as a range of test statistics such as relative risks and odds ratios. This dataset can be leveraged to quantitatively assess comorbidity, drug-drug, and drug-disease patterns for a range of clinical, epidemiological, and financial applications.
authors.affiliation: Center for Biomedical Informatics Research, Stanford University , Stanford, California 94305, USA.
authors.forename: Samuel G
authors.initials: SG
authors.lastname: Finlayson
authors.affiliation: Center for Biomedical Informatics Research, Stanford University , Stanford, California 94305, USA.
authors.forename: Paea
authors.initials: P
authors.lastname: LePendu
authors.affiliation: Center for Biomedical Informatics Research, Stanford University , Stanford, California 94305, USA.
authors.forename: Nigam H
authors.initials: NH
authors.lastname: Shah
mesh_terms: D015897:Comorbidity; D004347:Drug Interactions; D004358:Drug Therapy; D057286:Electronic Health Records; D006801:Humans; D008511:Medicine / Q000639:trends; D012306:Risk