{"doi":"10.1093/jamia/ocae211","title":"Towards cross-application model-agnostic federated cohort discovery","abstract":"OBJECTIVES: To demonstrate that 2 popular cohort discovery tools, Leaf and the Shared Health Research Information Network (SHRINE), are readily interoperable. Specifically, we adapted Leaf to interoperate and function as a node in a federated data network that uses SHRINE and dynamically generate queries for heterogeneous data models. MATERIALS AND METHODS: SHRINE queries are designed to run on the Informatics for Integrating Biology & the Bedside (i2b2) data model. We created functionality in Leaf to interoperate with a SHRINE data network and dynamically translate SHRINE queries to other data models. We randomly selected 500 past queries from the SHRINE-based national Evolve to Next-Gen Accrual to Clinical Trials (ENACT) network for evaluation, and an additional 100 queries to refine and debug Leaf's translation functionality. We created a script for Leaf to convert the terms in the SHRINE queries into equivalent structured query language (SQL) concepts, which were then executed on 2 other data models. RESULTS AND DISCUSSION: 91.1% of the generated queries for non-i2b2 models returned counts within 5% (or ±5 patients for counts under 100) of i2b2, with 91.3% recall. Of the 8.9% of queries that exceeded the 5% margin, 77 of 89 (86.5%) were due to errors introduced by the Python script or the extract-transform-load process, which are easily fixed in a production deployment. The remaining errors were due to Leaf's translation function, which was later fixed. CONCLUSION: Our results support that cohort discovery applications such as Leaf and SHRINE can interoperate in federated data networks with heterogeneous data models.","journal":"Journal of the American Medical Informatics Association","year":2024,"id":501953,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8768,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":225292,"name":"Michele Morris","orcid":"0000-0002-3255-5727","position":1,"is_corresponding":false},{"id":1162341,"name":"Eugene M. Sadhu","orcid":"0000-0001-9155-6730","position":2,"is_corresponding":false},{"id":814075,"name":"Douglas MacFadden","orcid":"0000-0001-8604-5231","position":3,"is_corresponding":false},{"id":1351778,"name":"Marc-Danie Nazaire","orcid":null,"position":4,"is_corresponding":false},{"id":1351779,"name":"William Walter Simons","orcid":null,"position":5,"is_corresponding":false},{"id":39725,"name":"Griffin M. Weber","orcid":"0000-0002-2597-881X","position":6,"is_corresponding":false},{"id":225293,"name":"Shawn N. Murphy","orcid":"0000-0002-1905-8806","position":7,"is_corresponding":false},{"id":225296,"name":"Shyam Visweswaran","orcid":"0000-0002-2079-8684","position":8,"is_corresponding":false},{"id":819785,"name":"Nicholas J Dobbins","orcid":"0000-0002-3598-8747","position":0,"is_corresponding":true}],"reference_count":30,"raw_metadata":null,"created_at":"2026-07-19T02:10:19.647285Z","pmid":"39110920","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}