{"id":"W3081320698","doi":"10.2196/20932","title":"Diagnosis of Type 2 Diabetes Using Electrogastrograms: Extraction and Genetic Algorithm–Based Selection of Informative Features","year":2020,"lang":"en","type":"article","venue":"JMIR Biomedical Engineering","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Diabetes mellitus; Selection (genetic algorithm); Medicine; Correlation; Feature selection; Type 2 diabetes; Pattern recognition (psychology); Artificial intelligence; Computer science; Mathematics; 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":[0.000907458,0.0004928196,0.0006229177,0.001804607,0.0002413579,0.0005668162,0.0003480872,0.0004410561,0.0004499494],"category_scores_gemma":[0.002347633,0.0001364755,0.0005541273,0.0006401297,0.0001712234,0.0002051031,0.0002681713,0.0003374435,0.000135171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003046063,"about_ca_system_score_gemma":0.0005087196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002422578,"about_ca_topic_score_gemma":0.001935629,"domain_scores_codex":[0.9996558,0.0001036444,0.00003570649,0.00009453706,0.00006158512,0.00004860502],"domain_scores_gemma":[0.9992356,0.0004656263,0.00009916334,0.00002408061,0.0001431907,0.00003237132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001104047,0.0009329881,0.1275224,0.0001319692,0.000378026,0.0006122541,0.0002180343,0.09475594,0.05233755,0.0006770785,0.001675113,0.7196547],"study_design_scores_gemma":[0.00009817859,0.0003237464,0.07345484,0.00004397934,0.0001822479,0.0004495516,0.0001129195,0.9142983,0.009500812,0.0009784952,0.0005261608,0.00003079161],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7502869,0.0004887867,0.2469531,0.0003016419,0.00003853646,0.0001693303,0.0002801397,0.0004225131,0.001059051],"genre_scores_gemma":[0.8819741,0.0001347065,0.1167598,0.0000719042,0.00002603894,0.0001133552,0.000512516,0.0000231198,0.0003844547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002422578,"threshold_uncertainty_score":0.004816949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008815867461069172,"score_gpt":0.2516337893962416,"score_spread":0.2428179219351724,"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."}}