{"id":"W2982348080","doi":"10.1109/wcnc.2019.8885802","title":"Artificial Intelligence for Diabetes Mellitus Type II: Forecasting and Anomaly Detection","year":2019,"lang":"en","type":"article","venue":"","topic":"Diabetes Management and Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Anomaly detection; Anomaly (physics); Computer science; Diabetes mellitus; Artificial intelligence; Medicine; Endocrinology; Physics","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":[0.001401449,0.0005236624,0.0005105126,0.001396132,0.0003248974,0.00111193,0.000895515,0.0007006113,0.0009233697],"category_scores_gemma":[0.004609968,0.0001606568,0.0005403112,0.001362111,0.0002487055,0.0009448018,0.0005424162,0.00122702,0.0004414931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004441036,"about_ca_system_score_gemma":0.0006455419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004281647,"about_ca_topic_score_gemma":0.002816039,"domain_scores_codex":[0.9993278,0.0001815834,0.00007521196,0.0001329193,0.0002395295,0.00004301392],"domain_scores_gemma":[0.9977722,0.001441619,0.0001904382,0.0001848536,0.0003364915,0.00007444641],"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":[0.0002080241,0.0005091022,0.02958186,0.0003014355,0.0002366038,0.0003764324,0.0001650919,0.1798295,0.005428143,0.01305468,0.01150901,0.7588001],"study_design_scores_gemma":[0.00001306828,0.00007160954,0.003689293,0.00005746749,0.00004742328,0.0001808217,0.00005128334,0.9730014,0.002415982,0.01610523,0.004345314,0.00002109533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06584968,0.005015344,0.9123614,0.003827372,0.0005101004,0.0002467768,0.001050985,0.003432752,0.007705603],"genre_scores_gemma":[0.6461855,0.003694023,0.3451586,0.0005129197,0.00047846,0.000166357,0.001293882,0.00007308705,0.002437217],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004281647,"threshold_uncertainty_score":0.008513451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06191706536411331,"score_gpt":0.3026136240735818,"score_spread":0.2406965587094685,"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."}}