{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01875717,0.0001092287,0.0005765964,0.00003200592,0.00003414609,0.00001522212,0.0006440849,0.0001992806,0.00001375459],"category_scores_gemma":[0.005040062,0.00006264758,0.00004353017,0.00009366398,0.000237695,0.000006792397,0.0004999908,0.0002948647,0.000003988248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000728431,"about_ca_system_score_gemma":0.0004478333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003236803,"about_ca_topic_score_gemma":0.0001151014,"domain_scores_codex":[0.9965142,0.0007319088,0.001891863,0.0001802489,0.0004785511,0.0002032144],"domain_scores_gemma":[0.9980794,0.0007153653,0.000393464,0.0006512444,0.00002862561,0.0001318764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001340325,0.000831894,0.1271177,0.0003835558,0.00009636689,0.00000283702,0.01084061,4.741761e-7,0.00001437731,0.0000487887,0.00357575,0.8569536],"study_design_scores_gemma":[0.01808011,0.006684577,0.334801,0.0009274968,0.0000751213,0.00008319362,0.222375,0.01157292,0.0004376724,0.0001129233,0.4039715,0.0008785541],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975021,0.0002424391,0.0008636111,0.0005235422,0.0001264568,0.0006767384,0.000009816312,0.000008433198,0.000046846],"genre_scores_gemma":[0.9943897,0.00009149597,0.004136071,0.001258307,0.00005611792,0.00001835133,0.00003787502,0.000004431461,0.000007625772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8560751,"threshold_uncertainty_score":0.6500897,"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."}}