{"id":"W3000947628","doi":"10.2196/14272","title":"Temporal Pattern Detection to Predict Adverse Events in Critical Care: Case Study With Acute Kidney Injury","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; Center for Clinical and Translational Science, University of Utah; University of Utah","keywords":"Classifier (UML); Artificial intelligence; Computer science; Random forest; Pattern recognition (psychology); Multivariate statistics; Acute kidney injury; Receiver operating characteristic; Intensive care; Data mining; Machine learning; Medicine; Intensive care medicine; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001149998,0.0004556285,0.0004646834,0.001164039,0.0006729471,0.0004958759,0.0004180191,0.000862905,0.0008148824],"category_scores_gemma":[0.005590794,0.000183566,0.0005413386,0.00065404,0.0003362287,0.0004699861,0.0004453674,0.0009219052,0.000168334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004434171,"about_ca_system_score_gemma":0.0006759089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004674484,"about_ca_topic_score_gemma":0.00766878,"domain_scores_codex":[0.999494,0.000143497,0.00007335377,0.0001057266,0.0001128222,0.00007056959],"domain_scores_gemma":[0.9976003,0.00122939,0.0004132135,0.0001104443,0.0003608194,0.0002858721],"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":[0.0004795943,0.0006357121,0.9271977,0.0001926857,0.000107501,0.03642188,0.0007632092,0.00262414,0.001724875,0.0001708234,0.001956486,0.02772536],"study_design_scores_gemma":[0.0001407377,0.001940389,0.8327102,0.0002373965,0.0002736229,0.07163433,0.003946729,0.07773639,0.005213621,0.001944216,0.004106331,0.000115872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951219,0.0004134597,0.002961268,0.0006520124,0.00002679394,0.0001157703,0.0002370024,0.00001763725,0.0004541001],"genre_scores_gemma":[0.9958603,0.0003727048,0.003101647,0.000131855,0.00007039664,0.00003943031,0.000272602,0.000005909643,0.0001451643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004674484,"threshold_uncertainty_score":0.009294569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01712322443671692,"score_gpt":0.2973875067924103,"score_spread":0.2802642823556934,"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."}}