{"id":"W4416663044","doi":"10.1016/j.artmed.2025.103316","title":"Using artificial intelligence to predict patient wait times in the emergency department: A scoping review","year":2025,"lang":"en","type":"review","venue":"Artificial Intelligence in Medicine","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Humber River Regional Hospital","funders":"","keywords":"Random forest; Feature selection; Selection (genetic algorithm); Feature (linguistics); Emergency department; Artificial neural network; Decision support system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002379034,0.0009847872,0.003781489,0.001097236,0.0002290257,0.00002114642,0.0008453064,0.000337396,0.001171206],"category_scores_gemma":[0.004712764,0.0006442504,0.0005748067,0.00502569,0.0003133612,0.0001152777,0.0003177992,0.00122496,0.0001576465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003533856,"about_ca_system_score_gemma":0.0007370248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000317239,"about_ca_topic_score_gemma":0.001027885,"domain_scores_codex":[0.9911875,0.0007031773,0.004774341,0.001242831,0.001095249,0.0009968944],"domain_scores_gemma":[0.9968465,0.0008845845,0.0006121545,0.001102057,0.0003311792,0.00022353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00004411066,0.0002951715,0.00001306275,0.06791312,0.0001038477,0.0002611433,0.001119022,0.00002495058,0.000001383186,0.005385401,0.001775447,0.9230633],"study_design_scores_gemma":[0.00002302797,0.0008034207,0.000001740034,0.9227123,0.001972463,0.000079706,0.002479633,0.0002346444,0.00004710081,0.006493237,0.06441204,0.0007406641],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002377081,0.9820005,0.003069913,0.002181503,0.001973371,0.008467703,0.00003823011,0.00005740804,0.002187569],"genre_scores_gemma":[0.0002823381,0.9955317,0.0004225697,0.001660762,0.0008008621,0.00107808,0.0001314969,0.0000575654,0.00003466307],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9223227,"threshold_uncertainty_score":0.9997419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1839331249187456,"score_gpt":0.4622341711221198,"score_spread":0.2783010462033743,"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."}}