{"id":"W3047416267","doi":"10.3390/info11080386","title":"Predicting Acute Kidney Injury: A Machine Learning Approach Using Electronic Health Records","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Acute Kidney Injury Research","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Logistic regression; Emergency department; Medicine; Health records; Emergency medicine; Acute kidney injury; Metric (unit); Healthcare Cost and Utilization Project; Health care; Predictive modelling; Retrospective cohort study; Medical emergency; Machine learning; Intensive care medicine; Internal medicine; Computer science","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":[0.000733814,0.0001612821,0.0002908071,0.0001948221,0.0002369474,0.00006897195,0.0001244236,0.00009743489,0.0001155536],"category_scores_gemma":[0.00087873,0.0001496888,0.00007173537,0.0006177321,0.00003027346,0.00108486,0.00009926356,0.0009515372,0.0001132824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004400459,"about_ca_system_score_gemma":0.001278136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001761207,"about_ca_topic_score_gemma":4.266634e-7,"domain_scores_codex":[0.9981414,0.0001058068,0.0005178441,0.0001581206,0.0005198023,0.0005570214],"domain_scores_gemma":[0.9987962,0.00001599826,0.000260546,0.0001659704,0.0001348532,0.0006264482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.009833016,0.0004386023,0.2651093,0.006347076,0.00223441,0.00002403417,0.06055715,0.0007976242,0.0144119,0.001810398,0.2929626,0.3454739],"study_design_scores_gemma":[0.001463871,0.001472976,0.0003781554,0.00007532216,0.0000646907,0.0001122793,0.0003158063,0.8614633,0.0006948729,0.00001225385,0.1337826,0.0001639079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5256926,0.0003214839,0.3149223,0.08789516,0.0005785094,0.006629203,0.001519011,0.002753624,0.05968815],"genre_scores_gemma":[0.97076,0.0001822949,0.008269442,0.01716463,0.0003720268,0.000047,0.002998786,0.00004015482,0.0001656295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8606657,"threshold_uncertainty_score":0.6104131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03163557583825543,"score_gpt":0.3278363932328936,"score_spread":0.2962008173946382,"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."}}