{"id":"W3096318863","doi":"10.1007/s10586-020-03199-8","title":"MMHGE: detecting mild cognitive impairment based on multi-atlas multi-view hybrid graph convolutional networks and ensemble learning","year":2020,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Computer science; Discriminative model; Cognitive impairment; Graph; Artificial intelligence; Pattern recognition (psychology); Convolutional neural network; Cognition; Machine learning; Medicine; Theoretical computer science","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":[0.001357727,0.001648679,0.001462443,0.002385718,0.0006440881,0.001089594,0.001689333,0.001476347,0.001443721],"category_scores_gemma":[0.002367932,0.0005501673,0.001645545,0.00133229,0.000277522,0.0009606892,0.002028773,0.001351654,0.0008538097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007855693,"about_ca_system_score_gemma":0.001190513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02997506,"about_ca_topic_score_gemma":0.04988379,"domain_scores_codex":[0.9994319,0.0001160951,0.00001795611,0.0001782686,0.0001393003,0.0001164027],"domain_scores_gemma":[0.9996005,0.0001180252,0.00003088736,0.00008820886,0.0001159313,0.0000465632],"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":[0.001309815,0.0008351002,0.03914369,0.0003109283,0.002897094,0.0007287764,0.0001873065,0.1379086,0.01992291,0.003120028,0.067565,0.7260708],"study_design_scores_gemma":[0.00005331209,0.0001601856,0.01268687,0.00003878278,0.0003647928,0.000634255,0.00007794252,0.9650593,0.009769407,0.006166905,0.004922308,0.00006607154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2621653,0.004484807,0.683978,0.001405727,0.0005603475,0.0004731656,0.01474488,0.02763112,0.004556623],"genre_scores_gemma":[0.7203309,0.001148364,0.24977,0.0006271375,0.0002201302,0.000283497,0.0183063,0.0009184913,0.008395199],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02997506,"threshold_uncertainty_score":0.05960119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03560893031235769,"score_gpt":0.3067903673563582,"score_spread":0.2711814370440006,"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."}}