{"id":"W3081757814","doi":"10.1109/embc44109.2020.9175448","title":"Predicting Acute Kidney Injury after Surgery","year":2020,"lang":"en","type":"article","venue":"","topic":"Acute Kidney Injury Research","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research","keywords":"Acute kidney injury; Logistic regression; Receiver operating characteristic; Medicine; Random forest; Medical record; Complication; Boosting (machine learning); Support vector machine; Emergency medicine; Artificial intelligence; Medical emergency; Machine learning; Intensive care medicine; Surgery; Internal medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001573467,0.0004086587,0.000502166,0.001320238,0.0002504564,0.0009391338,0.0003429271,0.0004331424,0.0008543981],"category_scores_gemma":[0.009440921,0.0001219296,0.0005914433,0.0009444076,0.000118807,0.000454325,0.0004515477,0.0006926255,0.0004066419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005500141,"about_ca_system_score_gemma":0.001085928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01347268,"about_ca_topic_score_gemma":0.02218037,"domain_scores_codex":[0.9992717,0.000209488,0.00008548085,0.0001310267,0.000182319,0.0001198662],"domain_scores_gemma":[0.9961887,0.001752411,0.0007720818,0.0001797564,0.0008269368,0.0002801828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001943131,0.0001820361,0.9636828,0.00003913543,0.00008663541,0.00005010727,0.0000371209,0.01124667,0.0002349203,0.00006183262,0.001187975,0.02299638],"study_design_scores_gemma":[0.0000323737,0.0007403994,0.7735256,0.0000897538,0.0001284971,0.0002243052,0.0004008476,0.2205154,0.001595496,0.0008801898,0.001834114,0.00003317501],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896951,0.0005118592,0.004890735,0.0005446474,0.00006396132,0.00008316671,0.002905419,0.0001301699,0.001175025],"genre_scores_gemma":[0.9924159,0.0002251062,0.003399883,0.0000574992,0.00002508354,0.00002272337,0.003586092,0.000004364348,0.0002633722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01347268,"threshold_uncertainty_score":0.02678853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03342048795509805,"score_gpt":0.3164393682288431,"score_spread":0.2830188802737451,"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."}}