{"id":"W4389991930","doi":"10.3390/diagnostics14010013","title":"Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer’s Disease","year":2023,"lang":"en","type":"article","venue":"Diagnostics","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Eisai; National Institute on Aging; Alzheimer's Association","keywords":"Cognitive impairment; Cognition; Machine learning; Disease; Artificial intelligence; Quality of life (healthcare); Alzheimer's disease; Computer science; Mini–Mental State Examination; Audiology; Medicine; Gerontology; Psychology; Physical medicine and rehabilitation; Internal medicine; Psychiatry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.005538316,0.0006324379,0.0008066442,0.001570433,0.0003509973,0.0007004827,0.0006965402,0.0006061751,0.0006715757],"category_scores_gemma":[0.01140184,0.0001829823,0.0006696207,0.0007241854,0.0001325841,0.0006930633,0.000598381,0.001084812,0.0003313955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006205598,"about_ca_system_score_gemma":0.001100874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005006046,"about_ca_topic_score_gemma":0.006596665,"domain_scores_codex":[0.9987838,0.0006702463,0.00008323022,0.0002349653,0.0001540435,0.00007368312],"domain_scores_gemma":[0.9952528,0.003310854,0.000383153,0.0002465525,0.0006687841,0.0001378568],"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":[0.00102647,0.0009366456,0.2821312,0.0001140145,0.0008187694,0.0002018261,0.000181264,0.2653668,0.003801568,0.001514811,0.005068023,0.4388386],"study_design_scores_gemma":[0.00001704448,0.000193503,0.01533973,0.00001483738,0.00005570022,0.00006957323,0.00001597746,0.9816455,0.0007132636,0.001449476,0.0004685817,0.0000167972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7143191,0.001990071,0.2770801,0.001043282,0.000194972,0.0001743441,0.001840897,0.001593426,0.001763676],"genre_scores_gemma":[0.9302595,0.000204779,0.06771761,0.0001407765,0.00006985803,0.00009085955,0.0009219603,0.00002868694,0.0005660646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005538316,"threshold_uncertainty_score":0.02928978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05291123259457695,"score_gpt":0.3528129661098521,"score_spread":0.2999017335152752,"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."}}