{"version":"1.0","last_updated":"2026-06-09T00:00:00Z","tools":[{"id":"keser","name":"KESER","tool_url":"https://shiny.parse-health.org/va/kesernetwork-linkage/","supports_bidirectional_linking":true,"link_discovery_api":"https://shiny.parse-health.org/va/api/tools/keser","description":"Knowledge Extraction via Sparse Embedding Regression (KESER) is a statistical method for extracting knowledge from large-scale EHR data."},{"id":"geopheno","name":"GeoPheno","tool_url":"https://shiny.parse-health.org/va/geopheno/","supports_bidirectional_linking":true,"link_discovery_api":"https://shiny.parse-health.org/va/api/tools/geopheno","description":"GeoPheno brings together and visualizes phenotype data from BRFSS, the Global Burden of Disease study, the U.S. Department of Veterans Affairs health system, and the Million Veteran Program. Explore prevalence by data source, phenotype, year, and location, and compare patterns across populations using maps and charts."},{"id":"phemega","name":"PheMEGA","tool_url":"https://shiny.parse-health.org/va/phemega/","supports_bidirectional_linking":false,"link_discovery_api":null,"description":"PheMEGA (Phenotype Multi-Ethnicity Genetic Architecture) explores genetic associations between SNPs and a wide range of phenotypes using genome-wide and phenome-wide association results from more than 635,000 Veterans enrolled in MVP. Users can search by SNP or phenotype, compare association signals across populations, and identify findings that are shared or differ from those observed in the European population. Interactive plots and tables make it easier to examine significant associations and population heterogeneity, supporting research into the genetic architecture of diverse populations."},{"id":"arch","name":"ARCH","tool_url":"https://app.parse-health.org/ARCH/","supports_bidirectional_linking":false,"link_discovery_api":null,"description":"ARCH Network uses electronic health record data to build a knowledge graph connecting more than 60,000 codified and clinical concepts, including diseases, medications, procedures, laboratory tests, and narrative-derived concepts. Users can search for a target concept and explore related features through interactive networks, hierarchies, tables, and similarity plots."},{"id":"loinc","name":"LOINC Hierarchy","tool_url":"https://shiny.parse-health.org/va/loinc-hierarchy/","supports_bidirectional_linking":false,"link_discovery_api":null,"description":"LOINC Hierarchy presents the relationships among LOINC laboratory and clinical observation codes in a structured, interactive format. Users can search and filter LOINC terms, explore their component and related parts in a hierarchical tree, and view each term’s code and description."},{"id":"rxnorm","name":"RxNorm Hierarchy","tool_url":"https://shiny.parse-health.org/va/rxnorm-hierarchy/","supports_bidirectional_linking":false,"link_discovery_api":null,"description":"RxNorm Hierarchy visualizes the relationships among medications, their branded and dosage-form variants, and related therapeutic classifications. Users can search RxNorm drugs, review key drug details, explore related medications, and view mappings to ATC and VA classes."},{"id":"phecodemap","name":"Phecode to ICD Map","tool_url":"https://shiny.parse-health.org/va/phecodemap-linkage/","supports_bidirectional_linking":true,"link_discovery_api":"https://shiny.parse-health.org/va/api/tools/phecodemap","description":"Phecode to ICD Map visualizes the mappings and hierarchical relationships between Phecodes and ICD-9-CM/ICD-10-CM diagnosis codes. The app helps users explore code mappings, parent-child relationships, and ICD rollups to identify relevant diagnosis codes for defining phenotypes in clinical studies."}]}