{"id":"W4390509856","doi":"10.3390/biomedinformatics4010006","title":"Biomedical Informatics: State of the Art, Challenges, and Opportunities","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Health informatics; Informatics; Engineering informatics; Multidisciplinary approach; Translational research informatics; Data science; Field (mathematics); Health Administration Informatics; Business informatics; Biomedicine; Translational bioinformatics; Computer science; Big data; Situated; Intersection (aeronautics); Health care; Artificial intelligence; Management science; Bioinformatics; Mathematics; Engineering; Data mining; Social science; Political science; Sociology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01988992,0.0008302381,0.001282898,0.004006429,0.002107864,0.01223906,0.002489257,0.006057666,0.006078308],"category_scores_gemma":[0.01952723,0.000682156,0.0007447047,0.004913888,0.008829693,0.02448846,0.004966148,0.009407291,0.002194998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002451099,"about_ca_system_score_gemma":0.006227068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001753904,"about_ca_topic_score_gemma":0.002199782,"domain_scores_codex":[0.9930607,0.003190472,0.0004963835,0.0008089043,0.002043605,0.0003999043],"domain_scores_gemma":[0.9501532,0.04011416,0.001224612,0.001678454,0.004928133,0.001901362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000125988,0.0001618905,0.002240739,0.005889122,0.0000528726,0.0001681703,0.001337361,0.001785687,0.0009259167,0.2488055,0.05074884,0.6877578],"study_design_scores_gemma":[0.00002487802,0.0001520146,0.001928799,0.01195156,0.00005996377,0.0009899983,0.005129096,0.008740313,0.0008046369,0.3842771,0.5857841,0.0001575889],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003154258,0.8107563,0.02414287,0.1470246,0.002875876,0.00003729878,0.000139998,0.0002070443,0.0116617],"genre_scores_gemma":[0.05170781,0.8893963,0.02893776,0.01464232,0.01231463,0.00009989731,0.0003163895,0.00009917834,0.002485784],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01988992,"threshold_uncertainty_score":0.1051892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05097690407849959,"score_gpt":0.2801951133638326,"score_spread":0.229218209285333,"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."}}