{"id":"W2900627007","doi":"10.1371/journal.pone.0206862","title":"Optimal intensive care outcome prediction over time using machine learning","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Thomas Hospital","funders":"NIHR Cambridge Biomedical Research Centre; University College London; National Institute for Health and Care Research","keywords":"Logistic regression; Intensive care unit; Machine learning; Predictability; Predictive modelling; Intensive care; Receiver operating characteristic; Medicine; Artificial intelligence; Predictive power; APACHE II; Regression; Computer science; Intensive care medicine; Statistics; Emergency medicine; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004506264,0.000124491,0.0002814778,0.00007625193,0.000102452,0.00001433724,0.00002934611,0.0000652137,0.0009518319],"category_scores_gemma":[0.000229035,0.0001028209,0.00006682017,0.00008772897,0.00004787235,0.00005667442,0.00004023092,0.0001477696,0.0003245016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001797685,"about_ca_system_score_gemma":0.00002174355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001138144,"about_ca_topic_score_gemma":0.000005479141,"domain_scores_codex":[0.9991425,0.00002303729,0.0001784864,0.0002155914,0.0002638635,0.0001765408],"domain_scores_gemma":[0.9991459,0.00003408604,0.00005742749,0.00016958,0.0005007712,0.00009226736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001028685,0.0005307997,0.9798301,0.00005336017,0.0005077658,0.00002992651,0.001056852,0.00002207568,0.01757059,0.000003097094,0.0001076745,0.0001848263],"study_design_scores_gemma":[0.006901811,0.005314864,0.6803887,0.001567483,0.004466096,0.00006254093,0.001396815,0.13642,0.1621794,0.000006585952,0.0008518493,0.0004438234],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980755,0.0004499191,0.0000153109,0.0002557143,0.00005430736,0.000284528,0.00002061963,0.0001056544,0.0007384215],"genre_scores_gemma":[0.9952779,0.00002411078,0.002881707,0.0006354296,0.0003943159,0.00001397195,0.00009508352,0.00002994668,0.0006474704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2994414,"threshold_uncertainty_score":0.9999614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1560085080149782,"score_gpt":0.3336014074164833,"score_spread":0.177592899401505,"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."}}