{"id":"W2903672000","doi":"10.1371/journal.pone.0209018","title":"Mucopolysaccharidosis type II detection by Naïve Bayes Classifier: An example of patient classification for a rare disease using electronic medical records from the Canadian Primary Care Sentinel Surveillance Network","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Queen's University","funders":"Shire Canada","keywords":"Naive Bayes classifier; Bayes' theorem; Mucopolysaccharidosis type II; Classifier (UML); Bayesian network; Artificial intelligence; Categorical variable; Disease; Medicine; Machine learning; Medical record; Computer science; Bayesian probability; Internal medicine; Enzyme replacement therapy; Support vector machine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002795028,0.0001385211,0.0001880816,0.0000401724,0.0005763274,0.00008064467,0.0005200708,0.0001176765,0.00002315796],"category_scores_gemma":[0.0001042188,0.0001200204,0.00004361862,0.0003390382,0.00008947469,0.0001854418,0.00008321883,0.0001756343,0.000002498061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002863049,"about_ca_system_score_gemma":0.001018258,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04287291,"about_ca_topic_score_gemma":0.1183689,"domain_scores_codex":[0.998279,0.0001551583,0.0002742428,0.0004540884,0.000429408,0.000408048],"domain_scores_gemma":[0.9983758,0.0001171857,0.0001613916,0.0005806406,0.0004818481,0.0002830865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001938116,0.005607723,0.2442097,0.00093969,0.001892599,0.00001285934,0.02423229,0.000489184,0.1339107,0.01802485,0.008978962,0.5597633],"study_design_scores_gemma":[0.0003083869,0.0005890271,0.01080253,0.000213359,0.00007545496,7.565564e-7,0.00009367216,0.9806575,0.004105178,0.00245885,0.0003715023,0.0003237461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8171592,0.0006739626,0.1810157,0.0004746316,0.0001960575,0.0003160062,0.00005992818,0.00006509779,0.0000393892],"genre_scores_gemma":[0.9950193,0.00004521087,0.003974737,0.0004321061,0.000322105,0.00003818363,0.0001439514,0.00001631864,0.000008077957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9801683,"threshold_uncertainty_score":0.9635007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06430013897306643,"score_gpt":0.2448939243251619,"score_spread":0.1805937853520955,"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."}}