{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003278245,0.0001817402,0.0002767198,0.0002178132,0.0001281289,0.00002326549,0.00007697388,0.00006217181,0.00006439083],"category_scores_gemma":[0.001247444,0.0001557121,0.0001317943,0.0003917379,0.00006756536,0.00005866457,0.0001341152,0.0001826685,0.00002911566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003653077,"about_ca_system_score_gemma":0.0001093428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003935047,"about_ca_topic_score_gemma":0.000001679532,"domain_scores_codex":[0.9984001,0.00006251821,0.000299124,0.0004100252,0.0004262177,0.0004020162],"domain_scores_gemma":[0.9983808,0.0005840128,0.00008951854,0.0001504133,0.0003983688,0.0003968395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00282345,0.001477745,0.969312,0.0002010248,0.0003966474,0.00003446865,0.0002782543,0.00004786433,0.0006845287,0.00001632573,0.002467889,0.02225977],"study_design_scores_gemma":[0.003862138,0.002828906,0.9419357,0.0004489298,0.0009875634,0.000001266677,0.0003010405,0.04345314,0.005471228,0.00007238043,0.0004958895,0.0001418864],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.972736,0.001274958,0.01614192,0.0008133703,0.0001910415,0.005671137,0.002625751,0.0002301614,0.0003156751],"genre_scores_gemma":[0.9898123,0.000461128,0.001904634,0.0001206724,0.0002151141,0.001064198,0.006197469,0.00004048375,0.000183958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04340528,"threshold_uncertainty_score":0.6349756,"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."}}