{"id":"W3124945631","doi":"10.2196/23888","title":"Noninvasive Real-Time Mortality Prediction in Intensive Care Units Based on Gradient Boosting Method: Model Development and Validation Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Intensive care; Gradient boosting; Feature engineering; Medicine; Computer science; Artificial intelligence; Receiver operating characteristic; Critically ill; Feature (linguistics); Machine learning; Data mining; Intensive care medicine; Deep learning; Random forest","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004629527,0.0001854259,0.0003785438,0.0001521173,0.00008147994,0.00002562562,0.00004586796,0.0001326986,0.00004591738],"category_scores_gemma":[0.000910831,0.0001490172,0.00003519751,0.00032335,0.00003291016,0.0000978434,0.00006620263,0.0002500855,0.00001246107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003449564,"about_ca_system_score_gemma":0.0007423674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003274926,"about_ca_topic_score_gemma":0.00003153456,"domain_scores_codex":[0.9979393,0.00009801739,0.0007118537,0.0001825657,0.000855094,0.0002131169],"domain_scores_gemma":[0.9983207,0.0002715392,0.0001464262,0.0002413808,0.0007478857,0.0002720861],"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":[0.0003798603,0.003755847,0.4707727,0.001642811,0.0007676748,0.001737719,0.419726,0.008148107,0.0001254623,0.0001415737,0.001809147,0.09099311],"study_design_scores_gemma":[0.007413076,0.0009248116,0.1655,0.0015936,0.0003019947,0.00007313465,0.122492,0.6876402,0.01363995,0.00001937262,0.0001114221,0.0002903326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963399,0.00002062848,0.0008641928,0.0002206242,0.00005697513,0.0009483325,0.00001703234,0.00005078985,0.001481516],"genre_scores_gemma":[0.9883619,0.0000240406,0.009234262,0.001445729,0.00002592989,0.0002679831,0.0005936177,0.00001373767,0.00003279169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6794922,"threshold_uncertainty_score":0.6076743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1183488478748363,"score_gpt":0.3871115901862883,"score_spread":0.268762742311452,"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."}}