{"id":"W3028704288","doi":"10.3390/informatics7020018","title":"Machine Learning for Identifying Medication-Associated Acute Kidney Injury","year":2020,"lang":"en","type":"article","venue":"Informatics","topic":"Acute Kidney Injury Research","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Medicine; Acute kidney injury; Medical prescription; Health care; Harm; Intensive care medicine; Logistic regression; Pharmacoepidemiology; Polypharmacy; MEDLINE; Retrospective cohort study; Health informatics; Population; Medical emergency; Emergency medicine; Data mining; Internal medicine; Public health; Environmental health; Computer science; Pharmacology; Nursing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.003114049,0.001355955,0.001145734,0.00522631,0.0005643955,0.001534965,0.0009330307,0.001204593,0.001798267],"category_scores_gemma":[0.01601261,0.0003287538,0.001414563,0.003680972,0.000351054,0.001087,0.0009835908,0.001585124,0.00110563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007799166,"about_ca_system_score_gemma":0.001637293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006400081,"about_ca_topic_score_gemma":0.004563995,"domain_scores_codex":[0.9970837,0.001145332,0.0004055235,0.0005830661,0.0006237314,0.0001585114],"domain_scores_gemma":[0.992765,0.005203048,0.0007326883,0.000520364,0.0006658307,0.0001130553],"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":[0.000344563,0.0008175079,0.1056701,0.0007673454,0.0009110405,0.0006810119,0.0002219367,0.1678279,0.002589706,0.007032433,0.01696268,0.6961738],"study_design_scores_gemma":[0.00003390644,0.0001325761,0.01483777,0.0001179721,0.0001033601,0.0002375328,0.0001235635,0.9547336,0.001424517,0.02297266,0.005236721,0.00004595116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1305083,0.008826646,0.8297781,0.003760116,0.0005198838,0.001011636,0.01232512,0.00673746,0.006532812],"genre_scores_gemma":[0.626907,0.002527723,0.3559161,0.0006571799,0.0004139073,0.0009326247,0.01062697,0.00009331821,0.001925296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006400081,"threshold_uncertainty_score":0.01646882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05269074474481727,"score_gpt":0.3646401468777998,"score_spread":0.3119494021329825,"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."}}