{"id":"W4378714206","doi":"10.1080/17477778.2023.2217334","title":"Machine learning integrated patient flow simulation: why and how?","year":2023,"lang":"en","type":"article","venue":"Journal of Simulation","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Inflow; Computer science; Machine learning; Flow (mathematics); Artificial intelligence; Construct (python library); Industrial engineering; Simulation; Operations research","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":[0.002878452,0.0005518576,0.0007229897,0.0004255575,0.0003790493,0.002216852,0.001316224,0.001516723,0.002201292],"category_scores_gemma":[0.008573821,0.0004859681,0.0006622375,0.0007455713,0.0008592313,0.002350431,0.001133781,0.001831472,0.0005531755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001644894,"about_ca_system_score_gemma":0.002362784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008855475,"about_ca_topic_score_gemma":0.006638508,"domain_scores_codex":[0.998672,0.0007912793,0.00006332199,0.000158519,0.0002244502,0.0000904953],"domain_scores_gemma":[0.9969302,0.002095845,0.0001872337,0.0001820619,0.000440285,0.0001643989],"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":[0.00006725363,0.00007432502,0.004158479,0.00008198559,0.0000487223,0.00004595257,0.0001188321,0.9161083,0.0003787921,0.02275128,0.001173204,0.05499282],"study_design_scores_gemma":[0.000008474123,0.00002360744,0.0002246405,0.0000277565,0.00000795174,0.00001334045,0.00002839574,0.9815295,0.0002246794,0.01615285,0.001750848,0.000007831925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03456233,0.0006082491,0.9489793,0.008118539,0.0001183715,0.0001522513,0.0001763796,0.0008399254,0.006444687],"genre_scores_gemma":[0.6691215,0.001274472,0.3256415,0.0008017677,0.0001138504,0.0002769667,0.0003239938,0.000111213,0.00233481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008855475,"threshold_uncertainty_score":0.01760781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08317975876338429,"score_gpt":0.4159317185976195,"score_spread":0.3327519598342352,"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."}}