{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006949726,0.0001271512,0.0001641233,0.0002530814,0.0000697969,0.000123101,0.0006729293,0.00006010082,0.000006094921],"category_scores_gemma":[0.00008000743,0.00007941382,0.00004670596,0.0002590241,0.0002463526,0.000682892,0.0005243541,0.000236211,0.00003377051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000270922,"about_ca_system_score_gemma":0.0002040462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003851294,"about_ca_topic_score_gemma":0.000001665774,"domain_scores_codex":[0.9985037,0.00003985364,0.0006212703,0.00007764203,0.0005210229,0.0002365021],"domain_scores_gemma":[0.9990026,0.0001942261,0.0001515044,0.0004483603,0.00006324029,0.0001400063],"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":[9.758826e-7,0.00001030445,0.00001670894,0.002500655,0.00002585627,0.00000423075,0.0318444,0.000005673886,0.000004759017,0.06997473,0.00430323,0.8913085],"study_design_scores_gemma":[0.0001168653,0.0001207257,0.0005640228,0.000416551,0.000007535391,0.0001035918,0.001201153,0.4048207,0.00004192907,0.003676814,0.5887787,0.000151362],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09506758,0.0638319,0.5321911,0.1768419,0.01827639,0.003268577,0.0003481952,0.003711202,0.1064631],"genre_scores_gemma":[0.8595933,0.02912697,0.1041499,0.003383469,0.0003643314,0.00006964045,0.00005528468,0.00006237815,0.003194637],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8911571,"threshold_uncertainty_score":0.3238401,"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."}}