OBJECTIVES: Direct electronic access to multiple electronic health record (EHR) systems through patient portals offers a novel avenue for decentralized research. Given the critical value of patient characterization, we sought to compare computable evaluation of health conditions from patient-portal EHR against the traditional self-report. MATERIALS AND METHODS: In the nationwide Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE) study, which linked self-reported questionnaires with multiplatform patient-portal EHR data, we compared self-reported health conditions across different clinical domains against computable definitions based on diagnosis codes, medications, vital signs, and laboratory testing. We assessed their concordance using Cohen's Kappa and the prognostic significance of differentially captured features as predictors of 1-year all-cause hospitalization risk. RESULTS: Among 1683 participants (mean age 41 ± 15 years, 67% female, 63% non-Hispanic Whites), the prevalence of conditions varied substantially between EHR and self-report (-13.2% to +11.6% across definitions). Compared with comprehensive EHR phenotypes, self-report under-captured all conditions, including hypertension (27.9% vs 16.2%), diabetes (10.1% vs 6.2%), and heart disease (8.5% vs 4.3%). However, diagnosis codes alone were insufficient. The risk for 1-year hospitalization was better defined by the same features from patient-portal EHR (area under the receiver operating curve [AUROC] 0.79) than from self-report (AUROC 0.68). DISCUSSION: EHR-derived computable phenotypes identified a higher prevalence of comorbidities than self-report, with prognostic value of additionally identified features. However, definitions based solely on diagnosis codes often undercaptured self-reported conditions, suggesting a role of broader EHR elements. CONCLUSION: In this nationwide study, patient-portal-derived EHR data enabled extensive capture of patient characteristics across multiple EHR platforms, allowing better disease phenotyping compared with self-report.

Motivation: The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers. Results: We propose an approach based on the singular value decomposition (SVD) that is able to combine data from across the knowledge graph through a reduced representation. Using cooccurrence data extracted from published literature, we show that SVD performs better than the leading methods for scoring discoveries. We also show the diminishing predictive power of knowledge discovery as we compare our predictions with real associations that appear further into the future. Finally, we examine the strengths and weaknesses of the SVD approach against another well-performing system using several predicted associations. Availability and implementation: All code and results files for this analysis can be accessed at https://github.com/jakelever/knowledgediscovery. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.