{"id":"W4229331796","doi":"10.12927/hcq.2022.26813","title":"Machine Learning Applied to Routinely Collected Health Administrative Data","year":2022,"lang":"en","type":"article","venue":"Healthcare Quarterly","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario","funders":"","keywords":"Computer science; Data science; Machine learning; Artificial intelligence; Process management; Knowledge management; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002284501,0.0004167243,0.00068152,0.0004252991,0.00246929,0.0002303705,0.003742327,0.00008050003,0.0001962551],"category_scores_gemma":[0.0001360152,0.00047022,0.00006857878,0.002388749,0.00003398484,0.0002896487,0.001161863,0.001993951,0.0001249729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007420756,"about_ca_system_score_gemma":0.002766851,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008634073,"about_ca_topic_score_gemma":0.003510204,"domain_scores_codex":[0.9925846,0.002109066,0.001066764,0.001779179,0.00118691,0.00127351],"domain_scores_gemma":[0.9953313,0.0003620963,0.0005687251,0.002610002,0.000176117,0.0009516872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003960608,0.0004723698,0.009109275,0.0006611415,0.00008791305,0.0001979378,0.08351471,0.002171932,0.00006684526,0.1332257,0.02803508,0.7420611],"study_design_scores_gemma":[0.002718387,0.02725879,0.034391,0.0001272698,0.00001413798,0.0006983479,0.01064456,0.2511785,0.00001023647,0.002550698,0.6682493,0.002158798],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01573282,0.004083911,0.3504256,0.5999068,0.004492957,0.009482664,0.00181339,0.006309484,0.007752383],"genre_scores_gemma":[0.9525269,0.000008939219,0.03243067,0.01288348,0.0001953162,0.0003999374,0.000735175,0.00006747201,0.0007521009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9367941,"threshold_uncertainty_score":0.9997749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07125663350019369,"score_gpt":0.3568292382445145,"score_spread":0.2855726047443208,"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."}}