{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004226282,0.0009723564,0.001016415,0.003767609,0.0005791346,0.001377417,0.0008420379,0.0009046549,0.0005319326],"category_scores_gemma":[0.01444392,0.0002890881,0.0009031834,0.002733794,0.0001623031,0.001663119,0.0008465633,0.0009563832,0.0003009754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007277369,"about_ca_system_score_gemma":0.001039839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01357071,"about_ca_topic_score_gemma":0.01224278,"domain_scores_codex":[0.997773,0.001052021,0.0002603954,0.0003782579,0.0004046792,0.0001316373],"domain_scores_gemma":[0.9940668,0.003889696,0.0005799443,0.0004079344,0.0009248258,0.0001308463],"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":[0.0004854724,0.001253066,0.342844,0.0001917451,0.001088678,0.000245658,0.0002383186,0.2394453,0.001693747,0.0007047225,0.002878417,0.4089309],"study_design_scores_gemma":[0.00002565007,0.0002302655,0.03065909,0.00005852855,0.0001799585,0.0001088401,0.000123785,0.9656025,0.0008499517,0.001504425,0.0006290144,0.00002790206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7802931,0.00208708,0.2089762,0.001759646,0.0001636851,0.0004501166,0.002377699,0.001163917,0.002728582],"genre_scores_gemma":[0.9256604,0.0005698337,0.07134236,0.0001519235,0.0001259526,0.0001192925,0.00163357,0.00001420716,0.0003824902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01357071,"threshold_uncertainty_score":0.0269835,"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."}}