{"id":"W4309091908","doi":"10.3390/app122111127","title":"An Interpretable Machine Learning Approach for Hepatitis B Diagnosis","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Interpretability; Machine learning; Artificial intelligence; AdaBoost; Decision tree; Gradient boosting; Logistic regression; Random forest; Boosting (machine learning); Computer science; Support vector machine; Medicine","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002640193,0.000125293,0.0002118008,0.0001313804,0.0074309,0.00002755157,0.0006754702,0.00006199119,0.001485005],"category_scores_gemma":[0.0001713482,0.0001155865,0.00004392826,0.0005761511,0.0002025692,0.0001574215,0.0002524564,0.000665203,0.00004612622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001677204,"about_ca_system_score_gemma":0.0002692867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005665321,"about_ca_topic_score_gemma":0.0009743235,"domain_scores_codex":[0.9975463,0.0004166312,0.0004355672,0.0005333195,0.0004121007,0.000656113],"domain_scores_gemma":[0.9984597,0.0009108838,0.0002029319,0.0002252181,0.00006611128,0.0001351746],"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":[0.0000685357,0.0001653788,0.8819474,0.0001312954,0.000008702573,8.451695e-7,0.01646081,0.0249762,0.0005926164,0.05196005,0.002133136,0.02155503],"study_design_scores_gemma":[0.0002472684,0.00126691,0.002067478,0.00002920067,0.0000226384,0.000002180919,0.080865,0.8223302,0.001165262,0.01343456,0.07799738,0.0005719721],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9202574,0.000413762,0.02957525,0.001411836,0.00129738,0.003807036,0.0001517617,0.0004533713,0.04263219],"genre_scores_gemma":[0.9829833,0.00003193623,0.007737206,0.001393211,0.0001671165,0.007212189,0.00005945818,0.00002077778,0.0003948246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8798799,"threshold_uncertainty_score":0.9994278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1484017627015627,"score_gpt":0.4545361366160857,"score_spread":0.306134373914523,"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."}}