{"id":"W3014315553","doi":"10.3233/jad-191169","title":"Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data","year":2020,"lang":"en","type":"article","venue":"Journal of Alzheimer s Disease","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":125,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hotchkiss Brain Institute; Alberta Health; University of Calgary","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Mathison Centre for Mental Health Research and Education; National Institutes of Health; U.S. Department of Defense; University of Calgary; Alzheimer's Disease Neuroimaging Initiative; Canada Research Chairs; Alzheimer Society","keywords":"Dementia; Neuroimaging; Receiver operating characteristic; Logistic regression; Cognition; Feature selection; Psychology; Cognitive impairment; Artificial intelligence; Machine learning; Medicine; Psychiatry; Disease; Internal medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002341239,0.000149723,0.0002648192,0.0001714419,0.00009915599,0.00009420574,0.0002288928,0.00001629056,0.0002232264],"category_scores_gemma":[0.0003479082,0.0001266021,0.00008167075,0.0002775699,0.00003607196,0.0003376434,0.0003903078,0.0003873559,0.0000131714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007617495,"about_ca_system_score_gemma":0.0002047254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002431366,"about_ca_topic_score_gemma":3.935029e-7,"domain_scores_codex":[0.9983197,0.0001409435,0.0003905518,0.0003088624,0.0005996971,0.0002401789],"domain_scores_gemma":[0.9981312,0.00004726623,0.0001749853,0.0002180111,0.0001577609,0.001270773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001032713,0.0001634879,0.9846147,0.00003176957,0.0008122957,0.0008307556,0.0001093221,0.00004331952,0.004372857,0.000001181668,0.0009280718,0.007059525],"study_design_scores_gemma":[0.002582157,0.0007049887,0.9358363,0.0001193677,0.006325132,0.00006898014,0.00009766645,0.04983878,0.0001921178,0.00001750231,0.004067033,0.0001500115],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9756052,0.01505072,0.001403521,0.007371025,0.000142686,0.0002589332,0.00007027918,0.00002125446,0.00007641358],"genre_scores_gemma":[0.9940212,0.0002301309,0.002292759,0.002828495,0.0005495277,8.123185e-7,0.00004140344,0.00003071992,0.000004973686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04979546,"threshold_uncertainty_score":0.5162684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0793542319470571,"score_gpt":0.3457734288888922,"score_spread":0.2664191969418351,"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."}}