{"id":"W4285341647","doi":"10.2196/34504","title":"Accurate Forecasting of Emergency Department Arrivals With Internet Search Index and Machine Learning Models: Model Development and Performance Evaluation","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Overcrowding; Computer science; Index (typography); The Internet; Emergency department; Machine learning; Triage; Artificial intelligence; Data mining; Operations research; Medical emergency; Medicine; Engineering","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":[0.003507372,0.001540979,0.001745387,0.001788608,0.0005342822,0.001226183,0.001347103,0.001411251,0.001316066],"category_scores_gemma":[0.006426001,0.0005192123,0.001187153,0.001304239,0.0004174767,0.001305193,0.000746024,0.001598259,0.0003416207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002116047,"about_ca_system_score_gemma":0.001821086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06381903,"about_ca_topic_score_gemma":0.01886459,"domain_scores_codex":[0.9993206,0.0002393515,0.00007856093,0.0001423091,0.0001137205,0.0001055045],"domain_scores_gemma":[0.9952028,0.003232235,0.0004025151,0.0001563707,0.0008775181,0.0001284633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001658825,0.0002012052,0.007632467,0.00005427757,0.00006171835,0.00003884229,0.0000296971,0.9723359,0.0001910849,0.0003660615,0.0005214422,0.01840146],"study_design_scores_gemma":[0.000003433374,0.00001723082,0.0003169326,0.000002090932,0.00000542526,0.000002106966,0.000003276023,0.9995131,0.00005267259,0.00006317523,0.00001811325,0.000002273231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8619384,0.002117674,0.1276356,0.001110855,0.0002165888,0.0003136973,0.000986509,0.00113791,0.004542767],"genre_scores_gemma":[0.9750676,0.0004697544,0.02250051,0.0000674514,0.0000588201,0.0001832329,0.0006930209,0.00002059973,0.0009390354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06381903,"threshold_uncertainty_score":0.1268951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1165652583631954,"score_gpt":0.3402947674657364,"score_spread":0.223729509102541,"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."}}