{"id":"W4401824861","doi":"10.1016/j.heliyon.2024.e36556","title":"Advancing thyroid care: An accurate trustworthy diagnostics system with interpretable AI and hybrid machine learning techniques","year":2024,"lang":"en","type":"article","venue":"Heliyon","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Lakehead University","funders":"","keywords":"Artificial intelligence; Machine learning; Feature selection; Computer science; Univariate; Triiodothyronine; Preprocessor; Classifier (UML); Naive Bayes classifier; Medical diagnosis; Thyroid; Data mining; Medicine; Internal medicine; Support vector machine; Pathology; Multivariate statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006440945,0.0002684366,0.0003547077,0.0001795009,0.0009142802,0.00008195722,0.0001588216,0.000167696,0.00006793136],"category_scores_gemma":[0.0002868423,0.0002141765,0.00003580521,0.0002528944,0.00007020045,0.0004898596,0.0001446958,0.001578316,0.0001288243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004217092,"about_ca_system_score_gemma":0.0003422546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001590164,"about_ca_topic_score_gemma":0.004420696,"domain_scores_codex":[0.9973608,0.0005704157,0.000594728,0.0005396414,0.0002458778,0.000688579],"domain_scores_gemma":[0.9981623,0.0008048666,0.000135894,0.0003417605,0.0002975678,0.0002576163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008913742,0.0001034559,0.5694957,0.07462005,0.0001565376,0.001669345,0.08491147,0.0009245526,0.00422492,0.0150443,0.001540391,0.246418],"study_design_scores_gemma":[0.0006645948,0.007196482,0.001889016,0.1161222,0.0004531794,0.0004954166,0.1908659,0.320178,0.04738877,0.0006075246,0.3109609,0.00317795],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.836884,0.1038289,0.04151401,0.002159403,0.002504906,0.003626127,0.000261861,0.004564749,0.00465607],"genre_scores_gemma":[0.9917504,0.005255449,0.001131461,0.0005761918,0.000489332,0.0002927913,0.0000625385,0.0001030083,0.0003388332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5676066,"threshold_uncertainty_score":0.8733866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02872069518708701,"score_gpt":0.3981940666927042,"score_spread":0.3694733715056172,"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."}}