{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002958802,0.0009461386,0.001001388,0.001413369,0.0002841631,0.001441055,0.0006369022,0.0007485746,0.001177882],"category_scores_gemma":[0.01215822,0.0002537973,0.0006567705,0.0009188741,0.0004077441,0.001023664,0.0007233698,0.00134414,0.0004432567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001123896,"about_ca_system_score_gemma":0.00146599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003505923,"about_ca_topic_score_gemma":0.002373253,"domain_scores_codex":[0.9990543,0.0003370589,0.00008524826,0.0002608972,0.0001610995,0.0001013393],"domain_scores_gemma":[0.9935098,0.004803018,0.0007579542,0.0001823171,0.0005536877,0.0001931334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006673227,0.0005126318,0.1314492,0.0002166965,0.0003719598,0.0002205225,0.0001354651,0.6589224,0.001297052,0.001378093,0.00283932,0.2019893],"study_design_scores_gemma":[0.00001243144,0.0001315776,0.01079523,0.00003538519,0.00002619372,0.00004790384,0.00002157279,0.9846702,0.0005228837,0.003442437,0.0002803988,0.00001380241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6133286,0.003729958,0.3749657,0.002281215,0.0001771999,0.0001678043,0.0009796714,0.001063721,0.00330601],"genre_scores_gemma":[0.9793489,0.0003841772,0.01896721,0.0001094809,0.00008068736,0.00007867652,0.0005565481,0.00002858478,0.0004457737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003505923,"threshold_uncertainty_score":0.01564783,"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."}}