{"id":"W4392108757","doi":"10.3389/fpubh.2024.1328353","title":"A feature optimization study based on a diabetes risk questionnaire","year":2024,"lang":"en","type":"article","venue":"Frontiers in Public Health","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Centers for Disease Control and Prevention; National Natural Science Foundation of China","keywords":"Medicine; Diabetes mellitus; Disease; Univariate; Feature selection; Standardization; Ranking (information retrieval); Risk factor; Computer science; Machine learning; Data mining; Environmental health; Internal medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001872382,0.0005784693,0.0005827425,0.0006998886,0.0002493535,0.000480755,0.0004723638,0.0004216607,0.0009169316],"category_scores_gemma":[0.00464703,0.0001382284,0.0008209234,0.0005660394,0.0001962247,0.0003833095,0.0003407484,0.0004045349,0.0001441033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005245763,"about_ca_system_score_gemma":0.0003564362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006899001,"about_ca_topic_score_gemma":0.002953559,"domain_scores_codex":[0.9993398,0.0002888264,0.00004790569,0.0001345192,0.0001142684,0.00007475541],"domain_scores_gemma":[0.9963691,0.002485932,0.0001476254,0.000283871,0.0006453676,0.00006811768],"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":[0.003326096,0.005769439,0.1528376,0.000821057,0.0006801733,0.0008416909,0.0004696395,0.3951176,0.02548224,0.002211924,0.00621936,0.4062231],"study_design_scores_gemma":[0.00008958082,0.001052019,0.04874774,0.00001636933,0.0001263383,0.000107862,0.0001961805,0.9422615,0.006283375,0.000295554,0.0007943604,0.00002918037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9701509,0.0001551483,0.02827615,0.00009346894,0.0000391407,0.0001345963,0.000475805,0.0001216141,0.000553113],"genre_scores_gemma":[0.9755288,0.00006629346,0.02246998,0.00002963194,0.00001514156,0.0001006855,0.001391616,0.00001026508,0.0003875475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006899001,"threshold_uncertainty_score":0.01371771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07253092882360608,"score_gpt":0.4257990989987154,"score_spread":0.3532681701751094,"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."}}