{"id":"W4292953849","doi":"10.2196/37658","title":"Predicting Abnormalities in Laboratory Values of Patients in the Intensive Care Unit Using Different Deep Learning Models: Comparative Study","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Documentation; Intensive care unit; Medical record; Medicine; Health care; Process (computing); Medical emergency; Test (biology); Computer science; Intensive care; Intensive care 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001128713,0.0001684125,0.0003635087,0.0002884817,0.0002700638,0.00004859862,0.001111973,0.00005887691,0.00001478823],"category_scores_gemma":[0.0003705053,0.0001272949,0.0000355389,0.0007985798,0.00008866514,0.0004878702,0.0009763986,0.001614402,6.696787e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001980258,"about_ca_system_score_gemma":0.0001917324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004032543,"about_ca_topic_score_gemma":0.0002318416,"domain_scores_codex":[0.9955624,0.001421058,0.0009738162,0.0001368967,0.001586967,0.0003189067],"domain_scores_gemma":[0.9981,0.0005907934,0.0004158445,0.0003255162,0.0004838981,0.00008388286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008500648,0.00009091742,0.4538218,0.0001079806,0.000005794949,0.000005489577,0.4977525,0.04729151,2.980883e-8,0.0002784917,0.000003451396,0.0006335049],"study_design_scores_gemma":[0.0004417035,0.0002802306,0.06556737,0.00006038248,0.000002324636,0.000001531892,0.4058366,0.5276682,6.850063e-7,0.00005961717,0.00001359061,0.00006772765],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996741,0.00008300567,0.00195705,0.00005792792,0.000154509,0.0007577597,0.000007883207,0.00004616132,0.0001947158],"genre_scores_gemma":[0.9989327,0.000004196584,0.0004772893,0.0004297017,0.0000162161,0.0001119965,0.00001949896,0.00000676106,0.000001642611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4803768,"threshold_uncertainty_score":0.7013863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06461473855195937,"score_gpt":0.3490707308358777,"score_spread":0.2844559922839183,"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."}}