{"id":"W4221154077","doi":"10.3233/jad-220021","title":"Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer’s Disease","year":2022,"lang":"en","type":"article","venue":"Journal of Alzheimer s Disease","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"National Center for Advancing Translational Sciences; National Institute of Neurological Disorders and Stroke; National Institute on Aging","keywords":"Neuroimaging; Dementia; Feature selection; Artificial intelligence; Classifier (UML); Magnetic resonance imaging; Computer science; Machine learning; Imaging genetics; Alzheimer's Disease Neuroimaging Initiative; Disease; Psychology; Medicine; Neuroscience; Pathology; Radiology","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":[0.001608404,0.0006590972,0.0006117836,0.002156096,0.0002311145,0.0005583953,0.0003898918,0.0005634513,0.001448002],"category_scores_gemma":[0.003866966,0.00008916319,0.0007167612,0.0006750103,0.0001076885,0.0003637884,0.000272672,0.000562873,0.000433776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004817539,"about_ca_system_score_gemma":0.0003910487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003764425,"about_ca_topic_score_gemma":0.003936312,"domain_scores_codex":[0.9995055,0.0001749216,0.00004409522,0.000121415,0.0001031542,0.00005081591],"domain_scores_gemma":[0.9983632,0.0007193864,0.000283377,0.0001044467,0.0004049799,0.0001245586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009830901,0.0004987897,0.8183405,0.00007763037,0.000640739,0.0002235556,0.00004619457,0.03820227,0.003562643,0.0002483338,0.004310262,0.132866],"study_design_scores_gemma":[0.00006044604,0.0005858717,0.3179628,0.00006704321,0.0004044427,0.0005482189,0.00007832779,0.6738262,0.003769034,0.001679964,0.0009670075,0.00005073226],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703081,0.0009062149,0.02285884,0.0005301919,0.00007607555,0.0001001301,0.002911722,0.0004270998,0.001881604],"genre_scores_gemma":[0.987425,0.0001452271,0.00980458,0.00005252212,0.00003480437,0.00004352675,0.00217817,0.000007989743,0.000308085],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003764425,"threshold_uncertainty_score":0.008506119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02917610050063706,"score_gpt":0.3049155008049313,"score_spread":0.2757394003042943,"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."}}