{"id":"W4200310839","doi":"10.1109/embc46164.2021.9629505","title":"Detecting Uncertainty of Mortality Prediction Using Confident Learning","year":2021,"lang":"en","type":"article","venue":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Class (philosophy); Outcome (game theory); Predictive modelling; Intensive care; Missing data; Patient care; Estimation; Intensive care unit","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001185198,0.0002007211,0.0004148385,0.000132017,0.00008302379,0.00001832416,0.0008941806,0.0001898116,0.00009573976],"category_scores_gemma":[0.001616443,0.000168848,0.0001703447,0.0006467093,0.0001980754,0.0001583829,0.0003395706,0.0008860672,0.000001180807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001504036,"about_ca_system_score_gemma":0.0002312883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001707535,"about_ca_topic_score_gemma":0.0002011376,"domain_scores_codex":[0.997894,0.0002828052,0.0007227701,0.0004262199,0.0003914512,0.0002827718],"domain_scores_gemma":[0.9978002,0.0004190296,0.0004059592,0.0004358556,0.0008752616,0.00006372423],"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":[0.00001509654,0.00008013004,0.2180404,0.0002909165,0.0003569229,0.000004677864,0.01007368,0.6196957,0.1411888,0.008051195,0.0003200124,0.001882483],"study_design_scores_gemma":[0.0006309215,0.00009859882,0.030144,0.0007870219,0.00002809113,0.00004027946,0.001108616,0.9617527,0.002972301,0.0005187447,0.001707384,0.0002114119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7803355,0.0001824044,0.2151341,0.001418467,0.002551084,0.0001256939,0.00002263661,0.0000505776,0.0001795467],"genre_scores_gemma":[0.9891641,0.0001578574,0.01011019,0.00009226912,0.0002815419,0.000008132525,0.00002959072,0.00001164757,0.0001446386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3420569,"threshold_uncertainty_score":0.6885422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06862648754161094,"score_gpt":0.3509250016623806,"score_spread":0.2822985141207697,"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."}}