{"id":"W2979709296","doi":"10.1109/jiot.2019.2946693","title":"CausalBG: Causal Recurrent Neural Network for the Blood Glucose Inference With IoT Platform","year":2019,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Diabetes Management and Research","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Inference; Causal inference; Artificial intelligence; Machine learning; Artificial neural network; Internet of Things; Recurrent neural network; Embedded system; Statistics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.000840554,0.0001666011,0.0003119308,0.0001050715,0.00007271236,0.0001143381,0.0003983998,0.0000613859,0.0002287635],"category_scores_gemma":[0.00008870199,0.00008738944,0.0001494448,0.0001360623,0.0001003989,0.0001833982,0.00008684791,0.0007289428,0.000020372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004024747,"about_ca_system_score_gemma":0.00008375003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000307541,"about_ca_topic_score_gemma":0.00001077072,"domain_scores_codex":[0.9984215,0.00002467242,0.0003597722,0.0001725277,0.0005640576,0.0004574488],"domain_scores_gemma":[0.9987468,0.0003398512,0.0002773144,0.0002361214,0.0002605359,0.0001394526],"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":[0.01420247,0.00213369,0.4679152,0.002414469,0.009138018,0.0005068118,0.007046635,0.008184447,0.01206392,0.005090445,0.1385399,0.332764],"study_design_scores_gemma":[0.05444651,0.06259589,0.1057843,0.01967418,0.006149617,0.002429797,0.001936739,0.5580723,0.06164473,0.005026332,0.1196639,0.002575692],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936294,0.0006224441,0.001348729,0.001048669,0.001463938,0.0006319925,0.00000175872,0.00001899494,0.001234078],"genre_scores_gemma":[0.9912652,0.00009710734,0.0008550122,0.0003923122,0.0006334131,0.0000112959,0.000003471526,0.0000264011,0.006715785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5498878,"threshold_uncertainty_score":0.3563638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03179145225098856,"score_gpt":0.3060910288448591,"score_spread":0.2742995765938705,"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."}}