RESUMEN
Rheumatoid arthritis (RA) accounts for one-fifth of the deaths due to arthritis, the leading cause of disability in the United States. Finding effective treatments for managing arthritis symptoms are a major challenge, since the mechanisms of autoimmune disorders are not fully understood and disease presentation differs for each patient. The American College of Rheumatology clinical guidelines for treatment consider the severity of the disease when deciding treatment, but do not include any prediction of drug efficacy. Using Electronic Health Records and Biomedical Linked Open Data (LOD), we demonstrate a method to classify patient outcomes using LASSO penalized regression. We show how Linked Data improves prediction and provides insight into how drug treatment regimes have different treatment outcome. Applying classifiers like this to decision support in clinical applications could decrease time to successful disease management, lessening a physical and financial burden on patients individually and the healthcare system as a whole.
RESUMEN
Meaningful Use guidelines have pushed the United States Healthcare System to adopt electronic health record systems (EHRs) at an unprecedented rate. Hospitals and medical centers are providing access to clinical data via clinical data warehouses such as i2b2, or Stanford's STRIDE database. In order to realize the potential of using these data for translational research, clinical data warehouses must be interoperable with standardized health terminologies, biomedical ontologies, and growing networks of Linked Open Data such as Bio2RDF. Applying the principles of Linked Data, we transformed a de-identified version of the STRIDE into a semantic clinical data warehouse containing visits, labs, diagnoses, prescriptions, and annotated clinical notes. We demonstrate the utility of this system though basic cohort selection, phenotypic profiling, and identification of disease genes. This work is significant in that it demonstrates the feasibility of using semantic web technologies to directly exploit existing biomedical ontologies and Linked Open Data.
RESUMEN
Post-market drug safety surveillance is hugely important and is a significant challenge despite the existence of adverse event (AE) reporting systems. Here we describe a preliminary analysis of search logs from healthcare professionals as a source for detecting adverse drug events. We annotate search log query terms with biomedical terminologies for drugs and events, and then perform a statistical analysis to identify associations among drugs and events within search sessions. We evaluate our approach using two different types of reference standards consisting of known adverse drug events (ADEs) and negative controls. Our approach achieves a discrimination accuracy of 0.85 in terms of the area under the receiver operator curve (AUC) for the reference set of well-established ADEs and an AUC of 0.68 for the reference set of recently labeled ADEs. We also find that the majority of associations in the reference sets have support in the search log data. Despite these promising results additional research is required to better understand users' search behavior, biasing factors, and the overall utility of analyzing healthcare professional search logs for drug safety surveillance.
