{"id":"W4214866510","doi":"10.2196/28781","title":"State of the Art of Machine Learning–Enabled Clinical Decision Support in Intensive Care Units: Literature Review","year":2022,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing Nova Program; National Key Research and Development Program of China; Beijing Municipal Science and Technology Commission","keywords":"Clinical decision support system; Decision support system; Artificial intelligence; Machine learning; Intensive care; Identification (biology); Point of care; Intensive care unit; Medicine; Computer science; Intensive care medicine; Nursing","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":[0.009600343,0.001191316,0.004011937,0.01998024,0.0008165856,0.003950239,0.002398251,0.002869695,0.004901795],"category_scores_gemma":[0.05529132,0.001022785,0.003852554,0.02001252,0.001204793,0.004994799,0.00170501,0.001696098,0.0008076857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003749143,"about_ca_system_score_gemma":0.01302314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004875621,"about_ca_topic_score_gemma":0.007402821,"domain_scores_codex":[0.9924975,0.001976608,0.0033571,0.0005480411,0.001379144,0.0002415913],"domain_scores_gemma":[0.8737855,0.1074254,0.01030425,0.001051402,0.00676441,0.0006690877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001702177,0.00004589933,0.001024653,0.6524444,0.001430989,0.0002326265,0.0003911193,0.0003825655,0.0002112323,0.001465049,0.008317221,0.333884],"study_design_scores_gemma":[0.00006277577,0.000151366,0.003456705,0.8819501,0.00697604,0.0008398752,0.0004736451,0.0003074381,0.0002771429,0.001777534,0.1036713,0.00005607384],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002182232,0.9986187,0.0001398389,0.0005048559,0.0001213265,0.00003242648,0.00007353828,0.000005398207,0.0002856974],"genre_scores_gemma":[0.00269681,0.9958697,0.0005509223,0.0004742234,0.0001907268,0.00006767428,0.00009658044,0.000003387337,0.0000499839],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01998024,"threshold_uncertainty_score":0.05077207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1065452537054574,"score_gpt":0.4465481936975651,"score_spread":0.3400029399921077,"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."}}