{"id":"W3003613697","doi":"10.5430/air.v8n2p15","title":"A simple classification framework for predicting Alzheimer’s disease from region-based grey matter volume and APOE genotype status","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Classifier (UML); Neuroimaging; Artificial intelligence; Computer science; Pattern recognition (psychology); Feature selection; Machine learning; Magnetic resonance imaging; Disease; Alzheimer's Disease Neuroimaging Initiative; Alzheimer's disease; Medicine; Psychology; Pathology; Neuroscience; Radiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0003359092,0.0001321643,0.0001239442,0.00004142307,0.0002630345,0.0001474619,0.0002213017,0.0001683198,0.00007453041],"category_scores_gemma":[0.0004683955,0.0001318748,0.00006405048,0.0001635499,0.0002023478,0.0000100663,0.0001272948,0.0002520308,0.00009052279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001628791,"about_ca_system_score_gemma":0.0001757313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001040003,"about_ca_topic_score_gemma":0.00003129302,"domain_scores_codex":[0.9984534,0.00008816268,0.0003267312,0.0004312864,0.0002110646,0.0004893637],"domain_scores_gemma":[0.9987968,0.0001721546,0.00007826982,0.0003319941,0.0002465664,0.0003742103],"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":[0.009123549,0.0005229844,0.2283918,0.0004476618,0.000597775,0.00002401307,0.004384605,0.006453619,0.07311448,0.02801889,0.04821185,0.6007088],"study_design_scores_gemma":[0.0001479733,0.000628453,0.008994404,0.00005693686,0.00007180992,6.372881e-7,0.001753463,0.845953,0.02543249,0.08998301,0.02647613,0.0005016881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.255835,0.001482485,0.7356288,0.005699785,0.000114413,0.0009133765,0.0001803534,0.00002395084,0.0001218129],"genre_scores_gemma":[0.9935607,0.0001167771,0.004272447,0.0008769033,0.0006539238,0.00008720574,0.0003868187,0.00002799948,0.00001721397],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8394994,"threshold_uncertainty_score":0.5377698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1569162251791973,"score_gpt":0.3779157151439693,"score_spread":0.220999489964772,"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."}}