{"id":"W2892005286","doi":"10.23889/ijpds.v3i4.840","title":"Machine learning: how much does it improve the prediction of unplanned hospital admissions?","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Logistic regression; Machine learning; Decision tree; Random forest; Predictive modelling; Primary care; Medical record; Artificial intelligence; Lasso (programming language); Medicine; Computer science; Family medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002107577,0.0001176915,0.0001130741,0.0002746064,0.0009210599,0.0006394135,0.005025906,0.00004436226,0.0000369532],"category_scores_gemma":[0.004958315,0.00007114399,0.00005178668,0.0004286917,0.0002049448,0.003118336,0.0009247931,0.0003458757,0.000004288459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001215487,"about_ca_system_score_gemma":0.000301952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002094883,"about_ca_topic_score_gemma":0.00006078877,"domain_scores_codex":[0.9974971,0.0001007512,0.0004189757,0.0004600071,0.001264919,0.0002582547],"domain_scores_gemma":[0.9969034,0.0002164328,0.0006406352,0.0006957451,0.001364597,0.0001791427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002684098,0.0003900174,0.5725239,0.00006247416,0.0001845217,0.00001698697,0.006110995,0.001685821,0.009502719,0.1291,0.01391227,0.2662418],"study_design_scores_gemma":[0.0003966646,0.0004438635,0.04854549,0.00006259558,0.000007637039,0.00007709715,0.0001650907,0.8978068,0.000741211,0.00495959,0.04667159,0.0001224167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.174777,0.0001098869,0.6230669,0.1707968,0.02885675,0.000923802,0.0009170558,0.0002081732,0.0003436543],"genre_scores_gemma":[0.9815652,0.00002899604,0.01676657,0.000250658,0.0008694121,0.00000564782,0.0001352953,0.000007717309,0.0003704369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8961209,"threshold_uncertainty_score":0.9339468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04399816139798792,"score_gpt":0.3665846899376288,"score_spread":0.3225865285396409,"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."}}