{"id":"W2969402850","doi":"10.2196/13917","title":"Building a Semantic Health Data Warehouse in the Context of Clinical Trials: Development and Usability Study","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data warehouse; Information retrieval; Ontology; Usability; Context (archaeology); SNOMED CT; Semantic interoperability; Health informatics; Semantic search; Terminology; Data science; World Wide Web; Semantic Web; Data mining; Medicine; Public health; Interoperability","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07669314,0.0009495639,0.001147913,0.003876536,0.001203725,0.005719208,0.002152305,0.001844657,0.001200341],"category_scores_gemma":[0.1032224,0.000966073,0.002177193,0.003308854,0.001206083,0.006956878,0.004227979,0.001710639,0.0004849952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001640474,"about_ca_system_score_gemma":0.004966798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00233982,"about_ca_topic_score_gemma":0.002153996,"domain_scores_codex":[0.9662097,0.02435907,0.004315065,0.001379622,0.003264353,0.0004722398],"domain_scores_gemma":[0.8729504,0.09587046,0.00271842,0.01225168,0.01453667,0.001672371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005577444,0.00779357,0.1016212,0.01436619,0.002151206,0.004929598,0.09638667,0.02363799,0.06061458,0.02350871,0.01967237,0.6397405],"study_design_scores_gemma":[0.003916841,0.01114289,0.1158339,0.009273475,0.003840557,0.008129314,0.08653369,0.3525437,0.0995213,0.03440966,0.2733345,0.001520157],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6448305,0.001355393,0.3232208,0.003045433,0.0002384051,0.009026586,0.005377039,0.007619728,0.005286078],"genre_scores_gemma":[0.3977181,0.0006084666,0.5935347,0.0004730818,0.00003761361,0.002273939,0.004502035,0.0004304706,0.0004215348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07669314,"threshold_uncertainty_score":0.4055969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1920129819161196,"score_gpt":0.4852674325602752,"score_spread":0.2932544506441557,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}