{"doi":"10.1093/jamiaopen/ooae076","title":"Leveraging multi-site electronic health data for characterization of subtypes: a pilot study of dementia in the N3C Clinical Tenant","abstract":"Objectives: To provide a foundational methodology for differentiating comorbidity patterns in subphenotypes through investigation of a multi-site dementia patient dataset. Materials and Methods: Employing the National Clinical Cohort Collaborative Tenant Pilot (N3C Clinical) dataset, our approach integrates machine learning algorithms-logistic regression and eXtreme Gradient Boosting (XGBoost)-with a diagnostic hierarchical model for nuanced classification of dementia subtypes based on comorbidities and gender. The methodology is enhanced by multi-site EHR data, implementing a hybrid sampling strategy combining 65% Synthetic Minority Over-sampling Technique (SMOTE), 35% Random Under-Sampling (RUS), and Tomek Links for class imbalance. The hierarchical model further refines the analysis, allowing for layered understanding of disease patterns. Results: The study identified significant comorbidity patterns associated with diagnosis of Alzheimer's, Vascular, and Lewy Body dementia subtypes. The classification models achieved accuracies up to 69% for Alzheimer's/Vascular dementia and highlighted challenges in distinguishing Dementia with Lewy Bodies. The hierarchical model elucidates the complexity of diagnosing Dementia with Lewy Bodies and reveals the potential impact of regional clinical practices on dementia classification. Conclusion: Our methodology underscores the importance of leveraging multi-site datasets and tailored sampling techniques for dementia research. This framework holds promise for extending to other disease subtypes, offering a pathway to more nuanced and generalizable insights into dementia and its complex interplay with comorbid conditions. Discussion: This study underscores the critical role of multi-site data analyzes in understanding the relationship between comorbidities and disease subtypes. By utilizing diverse healthcare data, we emphasize the need to consider site-specific differences in clinical practices and patient demographics. Despite challenges like class imbalance and variability in EHR data, our findings highlight the essential contribution of multi-site data to developing accurate and generalizable models for disease classification.","journal":"JAMIA Open","year":2024,"id":449043,"datarank":0.3463354695730015,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.0775715491887932,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.0775715491887932,"corpus_percentile":49.33859364121606,"corpus_rank":6550,"citation_count":5,"citer_count":5,"citers_with_citation_signal":4,"citers_with_endowment":4,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.8084,"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":6.25,"fair_percentile":25.068786303882604,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1268393,"name":"Jiebei Liu","orcid":"0009-0007-7412-6369","position":1,"is_corresponding":false},{"id":1268870,"name":"Amy Abramowitz","orcid":null,"position":2,"is_corresponding":false},{"id":1111036,"name":"Carol Geary","orcid":"0000-0003-1143-7147","position":3,"is_corresponding":false},{"id":398236,"name":"Karen C. Johnston","orcid":"0000-0003-3468-3121","position":4,"is_corresponding":false},{"id":737421,"name":"Carol A. Manning","orcid":"0000-0003-4205-2485","position":5,"is_corresponding":false},{"id":54506,"name":"John D. Van Horn","orcid":"0000-0003-1537-0816","position":6,"is_corresponding":false},{"id":948422,"name":"Andrea Zhou","orcid":"0000-0003-1845-5620","position":7,"is_corresponding":false},{"id":49884,"name":"Alfred J Anzalone","orcid":"0000-0002-3212-7845","position":8,"is_corresponding":false},{"id":59360,"name":"Johanna J. Loomba","orcid":"0000-0003-3673-5423","position":9,"is_corresponding":false},{"id":49914,"name":"Emily Pfaff","orcid":"0000-0002-6840-9756","position":10,"is_corresponding":false},{"id":455046,"name":"Donald D. Brown","orcid":"0000-0002-2740-2654","position":11,"is_corresponding":false},{"id":938522,"name":"Suchetha Sharma","orcid":null,"position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":null,"created_at":"2026-07-19T02:02:16.291892Z","pmid":"39132679","pmcid":"PMC11316614","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":22.2222,"fair_a":6.25,"fair_i":40.0,"fair_r":20.8333,"fair_zscore":-1.1162,"fair_rationale":{"fair_score":6.25,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":22.22,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"As per the N3C Clinical Tenant Pilot Data Usage Agreement (DUA), the source data cannot be shared publicly.","grounded":true,"rationale":"The paper provides no persistent identifier for its own dataset.","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit","RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier'","FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'"],"scored":true,"signal":null},{"key":"f_repository_named","label":"Named repository","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"As per the N3C Clinical Tenant Pilot Data Usage Agreement (DUA), the source data cannot be shared publicly.","grounded":true,"rationale":"The paper does not name a repository as the holder of its own data; the source data are not deposited.","anchors":["RDA-F4-01M — FAIR Data Maturity Model: metadata is offered so it can be harvested and indexed (","NIH DMS Policy Element 4 (NOT-OD-21-014) — name the repository where data will be archived","NSTC Desirable Characteristics of Data Repositories (2022) — 'Long-Term Sustainability', 'Reten"],"scored":true,"signal":null},{"key":"f_data_availability_statement","label":"Data-availability statement","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"As per the N3C Clinical Tenant Pilot Data Usage Agreement (DUA), the source data cannot be shared publicly.","grounded":true,"rationale":"The data availability statement declares the data cannot be shared publicly and offers no route, matching Colavizza category 0.","anchors":["Colavizza, Hrynaszkiewicz, Staden, Whitaker & McGillivray (2020), 'The citation advantage of li","Springer Nature research data policy — Data Availability Statements: standard statement templat","RDA-F3-01M — metadata clearly and explicitly includes the identifier of the data it describes"],"scored":false,"signal":null},{"key":"f_discovery_metadata","label":"Description of the dataset as an object","kind":"llm","weight":2.0,"fraction":1.0,"verdict":"yes","evidence":"Table 1. 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For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","Remove the precondition or justify it. Release the data at publication with no embargo, no registration wall, and no approval step — NIH's zero-embargo public- access rule (NOT-OD-25-101) has already made 'available at publication' the federal baseline for the article; the data should not lag behind it. For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the clinical / human-subjects repository accession (e.g. from dbGaP or the European Genome-phenome Archive (EGA)) in the reference list."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"epmc_xml"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"epmc_xml","fair_has_llm":true,"fair_computed_at":"2026-07-20T12:57:38.615795Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}