{"id":"W3008725318","doi":"10.2196/13075","title":"Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Emergency department; Medicine; Retrospective cohort study; Emergency medicine; Cohort; Outpatient clinic; Medical emergency; Respiratory system; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006348954,0.0002220214,0.0003247347,0.0000283853,0.0004011656,0.00003391251,0.0001617824,0.00007402409,0.0001685967],"category_scores_gemma":[0.0007512188,0.0001720616,0.00005094604,0.0002125752,0.0001177642,0.0003135505,0.0003377269,0.0003268234,0.000006089904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003674069,"about_ca_system_score_gemma":0.00003194714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004192137,"about_ca_topic_score_gemma":0.00001721018,"domain_scores_codex":[0.9976563,0.0001392025,0.0006679451,0.0002457072,0.0009326147,0.0003582304],"domain_scores_gemma":[0.9988847,0.0001258912,0.0003657468,0.0001405656,0.00004089653,0.0004421328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000597781,0.0001722361,0.9764361,0.0001041731,0.00004646918,0.000001625899,0.005248376,0.0007654632,0.000001718109,0.000001770597,0.0000529297,0.0171094],"study_design_scores_gemma":[0.001531168,0.003305088,0.5437111,0.0000799814,0.0001089284,0.000001165188,0.001199481,0.4480313,0.000008787625,0.00001325013,0.001686468,0.0003232485],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9758029,0.00004297572,0.02232558,0.00002200453,0.0001077651,0.001533777,0.00002356676,0.00007486135,0.00006656034],"genre_scores_gemma":[0.9861808,0.000005497959,0.0133372,0.0001083438,0.0001494486,0.0001505739,0.00003395063,0.00002652895,0.000007624852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4472659,"threshold_uncertainty_score":0.7016469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03752055049348144,"score_gpt":0.3242581136832026,"score_spread":0.2867375631897212,"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."}}