{"id":"W4399311680","doi":"10.2196/55793","title":"A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation","year":2024,"lang":"en","type":"article","venue":"JMIR Nursing","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institutes of Health","keywords":"Preprint; Computer science; Scalability; Audit; Logical data model; Data mining; Data science; Audit trail; Database; World Wide Web; Data modeling; Business; Accounting","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":[0.007579515,0.001105656,0.000979332,0.002976889,0.0007707642,0.004690062,0.003855821,0.001285834,0.001798202],"category_scores_gemma":[0.02311603,0.001092631,0.00336327,0.002943998,0.001357875,0.005423198,0.002700676,0.002683241,0.0006405968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003559923,"about_ca_system_score_gemma":0.005773917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02259486,"about_ca_topic_score_gemma":0.02229638,"domain_scores_codex":[0.9951261,0.001438744,0.0007966745,0.001007629,0.001342669,0.0002882676],"domain_scores_gemma":[0.9846315,0.009583752,0.001711597,0.001148748,0.002618253,0.000306121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001275784,0.0002405067,0.007624906,0.0004311327,0.0001516639,0.0004477034,0.0007007404,0.8173293,0.001736749,0.1138849,0.00353562,0.05378919],"study_design_scores_gemma":[0.000008156216,0.00002274064,0.0002572527,0.00003432906,0.00002158238,0.00004528978,0.00006489287,0.9802786,0.0003141196,0.01698791,0.001953508,0.00001170805],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005614376,0.0001526814,0.9904626,0.0006762836,0.00003306603,0.0002472403,0.001573373,0.0005501803,0.0006903032],"genre_scores_gemma":[0.1654362,0.000627824,0.8257123,0.000332898,0.0001011122,0.001582638,0.004622314,0.0001229949,0.001461679],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02259486,"threshold_uncertainty_score":0.0449267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1245623597620613,"score_gpt":0.3645623445886479,"score_spread":0.2399999848265866,"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."}}