{"id":"W4407828145","doi":"10.3233/shti200528","title":"Using Long Short-Term Memory (LSTM) Neural Networks to Predict Emergency Department Wait Time","year":2020,"lang":"en","type":"book-chapter","venue":"Studies in health technology and informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Emergency department; Long short term memory; Computer science; Term (time); Artificial neural network; Recurrent neural network; Artificial intelligence; Machine learning; Medical emergency; Medicine; Nursing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003386266,0.0006274086,0.0002254836,0.0003944786,0.0001482847,0.0005314876,0.0004386207,0.000545488,0.002859759],"category_scores_gemma":[0.001452665,0.0001941917,0.0003120709,0.0006395805,0.00006486466,0.0008766777,0.0002065105,0.000805629,0.0009238665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000499883,"about_ca_system_score_gemma":0.0004261441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008448259,"about_ca_topic_score_gemma":0.01700985,"domain_scores_codex":[0.9999256,0.00001192031,0.000005000142,0.00002532338,0.0000170056,0.0000151315],"domain_scores_gemma":[0.9997017,0.0001912716,0.000026948,0.000009660683,0.00005983591,0.00001065868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003698361,0.0002761467,0.02010464,0.0001811885,0.0001600704,0.0002150116,0.0001102701,0.2626481,0.007633088,0.003179213,0.02087696,0.6842456],"study_design_scores_gemma":[0.000007100586,0.00005965125,0.003496607,0.00002899777,0.00003859677,0.0000452383,0.00002569785,0.9899384,0.00212192,0.00296595,0.00126027,0.00001165406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4668437,0.006615065,0.4893135,0.002523456,0.001673284,0.0000679415,0.003718654,0.003511791,0.02573258],"genre_scores_gemma":[0.906076,0.001457963,0.07389679,0.0002688465,0.0002051926,0.00005441644,0.002029093,0.00009945394,0.01591217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008448259,"threshold_uncertainty_score":0.0167982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06105976934652597,"score_gpt":0.3495464511369246,"score_spread":0.2884866817903987,"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."}}