{"id":"W3000673289","doi":"10.1109/aiam48774.2019.00091","title":"Diagnosis of Female Diabetic Patients Based on Artificial Intelligence","year":2019,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Earl Haig Secondary School","funders":"","keywords":"Support vector machine; Diabetes mellitus; Logistic regression; Artificial intelligence; Machine learning; Computer science; Set (abstract data type); Test set; Medicine; Endocrinology","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":[0.0006943796,0.0004946506,0.0005864908,0.002567679,0.0003069941,0.0008519714,0.0004288541,0.0005155612,0.001211407],"category_scores_gemma":[0.003254347,0.0001391048,0.0006262831,0.0008874561,0.0001416258,0.000562019,0.0004459649,0.0004067968,0.000522687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000286025,"about_ca_system_score_gemma":0.0003309169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002036835,"about_ca_topic_score_gemma":0.001663505,"domain_scores_codex":[0.9992579,0.0001921146,0.0001030402,0.0001275389,0.0002542141,0.00006518536],"domain_scores_gemma":[0.9993201,0.0002800474,0.0000914944,0.00005264123,0.0002126341,0.00004302262],"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":[0.0006932415,0.0004022818,0.372447,0.0003531932,0.0002262447,0.001697552,0.0004633848,0.007191752,0.007632184,0.001380778,0.008009864,0.5995026],"study_design_scores_gemma":[0.0001998284,0.001500622,0.3805996,0.0005436166,0.001028548,0.01085949,0.002354813,0.551173,0.02275911,0.009327884,0.01943647,0.0002169212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.826068,0.006773023,0.1383063,0.003640692,0.0004367428,0.0004489523,0.002087004,0.001488408,0.02075084],"genre_scores_gemma":[0.9568796,0.001621467,0.03803593,0.0003603784,0.0001570338,0.00008194509,0.000977047,0.00001674306,0.001869949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002567679,"threshold_uncertainty_score":0.004052579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1533515316332899,"score_gpt":0.449016864286625,"score_spread":0.2956653326533352,"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."}}