{"id":"W3120982506","doi":"10.1109/comnet47917.2020.9306071","title":"Diabetes Analytics and Recommendation Engine (DARE)","year":2020,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Context (archaeology); Computer science; Diabetes mellitus; Analytics; Architecture; Anomaly detection; Modular design; Data science; Medicine; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002400413,0.0000758001,0.0001358533,0.00003117051,0.0003034224,0.000005981175,0.00005812511,0.00009780665,0.00276994],"category_scores_gemma":[0.0005544815,0.00006610014,0.0000160697,0.0001691094,0.00002401556,0.00009058874,0.00006956343,0.0003211108,0.0006887338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003882374,"about_ca_system_score_gemma":0.00006214614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001704966,"about_ca_topic_score_gemma":0.0002461538,"domain_scores_codex":[0.9989868,0.0001457715,0.0003511535,0.0001764397,0.00007135814,0.0002684576],"domain_scores_gemma":[0.999044,0.0004738459,0.00007099895,0.00008908845,0.000116722,0.0002053402],"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":[0.00001868664,0.0000152756,0.8735068,0.0004370468,0.00001805075,0.000001023986,0.006462049,0.0000226817,0.0005812813,0.004520531,0.02940428,0.08501224],"study_design_scores_gemma":[0.0004245689,0.0005398986,0.04322146,0.0002461259,0.00005853113,3.468364e-7,0.03406446,0.5680318,0.00512934,0.005964642,0.3416327,0.0006861134],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8198474,0.0001167016,0.004872401,0.1621152,0.0005157204,0.0007430718,0.000020527,0.0005376893,0.0112313],"genre_scores_gemma":[0.9837953,0.00007238377,0.001134764,0.01429473,0.0003433554,0.00002100756,0.0000154894,0.00001926125,0.0003036397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8302854,"threshold_uncertainty_score":0.9981416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2481330447985177,"score_gpt":0.4774274551418052,"score_spread":0.2292944103432876,"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."}}