{"id":"W4224055553","doi":"10.1108/imds-12-2021-0769","title":"Making the hospital smart: using a deep long short-term memory model to predict hospital performance metrics","year":2022,"lang":"en","type":"article","venue":"Industrial Management & Data Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Recurrent neural network; Computer science; Autoregressive integrated moving average; Mean squared error; Mean absolute percentage error; Medical prescription; Autoregressive model; Time series; Artificial intelligence; Term (time); Artificial neural network; Machine learning; Data mining; Statistics; Medicine; Mathematics","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":[0.0007112121,0.0008311995,0.0003868086,0.0007802377,0.0001630825,0.000843291,0.0009025451,0.0006913432,0.00126643],"category_scores_gemma":[0.002532643,0.0002148115,0.0003946022,0.000532666,0.0002160843,0.001061453,0.0006433719,0.0007918257,0.000375799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009180274,"about_ca_system_score_gemma":0.0009385223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01607934,"about_ca_topic_score_gemma":0.01591443,"domain_scores_codex":[0.9997403,0.00005936112,0.00002115329,0.00008109495,0.00005000419,0.00004807806],"domain_scores_gemma":[0.9995028,0.0001480671,0.000123485,0.00003063926,0.0001385131,0.00005643814],"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":[0.0005394438,0.0006472779,0.1262724,0.0002394432,0.0002813993,0.0004888896,0.0003412221,0.5830246,0.004611501,0.003157665,0.008731476,0.2716646],"study_design_scores_gemma":[0.000005413842,0.00006431713,0.004858297,0.00001833811,0.00001986651,0.00001623272,0.00003417884,0.9927207,0.0005060912,0.001435989,0.0003105539,0.00001001913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7885535,0.001867991,0.197609,0.003061377,0.0002652408,0.0001110891,0.001874129,0.00160837,0.005049355],"genre_scores_gemma":[0.9875633,0.0002346924,0.01032807,0.0001532397,0.00003614208,0.0000315729,0.0006534974,0.00001668621,0.0009828572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01607934,"threshold_uncertainty_score":0.03197151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1019509993021668,"score_gpt":0.2622060953328756,"score_spread":0.1602550960307089,"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."}}