{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003378401,0.0009520343,0.00074465,0.003108614,0.0002695534,0.001089331,0.0005814898,0.0008487194,0.0008543541],"category_scores_gemma":[0.009825503,0.0002174138,0.0008846258,0.001227386,0.0003179523,0.0007006874,0.0005217631,0.001001916,0.0005857986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007031648,"about_ca_system_score_gemma":0.0007291179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002776802,"about_ca_topic_score_gemma":0.002594475,"domain_scores_codex":[0.9989536,0.0004886977,0.0001162881,0.0002040527,0.0001444722,0.00009288618],"domain_scores_gemma":[0.9919586,0.006398329,0.0006930794,0.0002501871,0.0005306141,0.0001692355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008574581,0.0008303694,0.6380484,0.0001954598,0.0008162439,0.0002810481,0.0001147131,0.1495445,0.001805698,0.0004406393,0.002972054,0.2040934],"study_design_scores_gemma":[0.00004659478,0.0003441719,0.06573113,0.00006659027,0.0001296349,0.0002494352,0.00006240526,0.9271563,0.001709931,0.003822895,0.0006444657,0.00003636666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8637462,0.001856922,0.1273521,0.001197003,0.0001069832,0.0002169896,0.002627483,0.0009945843,0.001901726],"genre_scores_gemma":[0.975991,0.0002140403,0.02157646,0.00008944074,0.00006711374,0.0001112513,0.001669648,0.00001807775,0.0002630748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003378401,"threshold_uncertainty_score":0.01786691,"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."}}