{"id":"W3083333873","doi":"10.3233/jad-191340","title":"Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment","year":2020,"lang":"en","type":"article","venue":"Journal of Alzheimer s Disease","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Naive Bayes classifier; Receiver operating characteristic; Montreal Cognitive Assessment; Machine learning; Cognition; Artificial intelligence; Cognitive impairment; Demographics; Medicine; Computer science; Support vector machine; Psychiatry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01488487,0.002017554,0.001214738,0.00386725,0.000599369,0.002088963,0.000927688,0.001244428,0.0008865205],"category_scores_gemma":[0.02339032,0.0003184528,0.001531931,0.001107786,0.0007211335,0.001670913,0.001170491,0.001357127,0.0004895605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009298031,"about_ca_system_score_gemma":0.001174923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0048315,"about_ca_topic_score_gemma":0.002983059,"domain_scores_codex":[0.9959,0.002748534,0.0002746716,0.0005090407,0.000425486,0.0001422333],"domain_scores_gemma":[0.9820908,0.01439684,0.001236315,0.0008235017,0.001178839,0.0002736866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001666085,0.0008471781,0.4396631,0.0002931934,0.001961747,0.0003036335,0.000589176,0.2551838,0.001950582,0.001454827,0.003462942,0.2926238],"study_design_scores_gemma":[0.00002801084,0.0007661838,0.0268933,0.0000910228,0.0001578986,0.0001756062,0.000158018,0.9663481,0.001690643,0.003115812,0.0005235119,0.00005177926],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8328052,0.003254398,0.1560408,0.001424118,0.000181365,0.0003713763,0.001014429,0.001543031,0.003365374],"genre_scores_gemma":[0.9764267,0.0003646481,0.02189713,0.0001269902,0.00009164546,0.0001212522,0.0004137969,0.00002597206,0.0005318725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01488487,"threshold_uncertainty_score":0.07871968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1065485724358504,"score_gpt":0.3465191988746329,"score_spread":0.2399706264387825,"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."}}