{"id":"W4378418319","doi":"10.18280/ria.370220","title":"An Intelligent Evolutionary Schema on Precision Medicine for Diabetes Using Big Data Analytics","year":2023,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Schema (genetic algorithms); Big data; Computer science; Analytics; Data science; Data analysis; Precision medicine; Diabetes mellitus; Data mining; Machine learning; Medicine; Endocrinology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001282716,0.0003727206,0.0003521461,0.001277616,0.001378002,0.002127439,0.001482287,0.001135638,0.003675478],"category_scores_gemma":[0.002739515,0.0002345885,0.0008898701,0.001319557,0.0009626379,0.002401983,0.002013538,0.00099141,0.000662658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001318298,"about_ca_system_score_gemma":0.002525274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006636629,"about_ca_topic_score_gemma":0.007018558,"domain_scores_codex":[0.9991246,0.000201001,0.00007393714,0.0002362519,0.0002884725,0.00007570632],"domain_scores_gemma":[0.999213,0.0001819996,0.00007366918,0.0001480387,0.000286552,0.00009672112],"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":[0.000125653,0.0002989856,0.01097213,0.0002766816,0.0001121225,0.0008383515,0.001385265,0.05405544,0.006909643,0.4455447,0.01844223,0.4610389],"study_design_scores_gemma":[0.00005512708,0.0001685472,0.005140472,0.0002126915,0.0001389876,0.001127932,0.000894309,0.6341659,0.009828172,0.2231051,0.1250852,0.00007763458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02684873,0.0008230717,0.9354392,0.005329928,0.0002828169,0.0003075475,0.0002885535,0.0009256079,0.02975453],"genre_scores_gemma":[0.2344409,0.0008375328,0.7503091,0.0008769687,0.0001059948,0.0002014444,0.000562634,0.00008874335,0.0125768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006636629,"threshold_uncertainty_score":0.01319599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5535323450164978,"score_gpt":0.5117232649872918,"score_spread":0.04180908002920602,"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."}}