{"id":"W4386895318","doi":"10.3390/app131810470","title":"Enhancing Early Dementia Detection: A Machine Learning Approach Leveraging Cognitive and Neuroimaging Features for Optimal Predictive Performance","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":10,"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; Eisai; Janssen Alzheimer Immunotherapy Research And Development; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; F. Hoffmann-La Roche; University of Southern California; Bristol-Myers Squibb; Eli Lilly and Company; Biogen; BioClinica; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Machine learning; Artificial intelligence; Dementia; Neuroimaging; Computer science; Support vector machine; Cognition; AdaBoost; Artificial neural network; Benchmark (surveying); Naive Bayes classifier; Psychology; Disease; Medicine; Neuroscience","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":[0.00228492,0.001149859,0.001170787,0.002044374,0.0003502629,0.001035458,0.0009423479,0.0009847197,0.000719855],"category_scores_gemma":[0.004258328,0.0002753101,0.0007789299,0.0008466278,0.0002608897,0.001016219,0.0007124158,0.001457807,0.0005410213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000395031,"about_ca_system_score_gemma":0.000973984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003426323,"about_ca_topic_score_gemma":0.004058952,"domain_scores_codex":[0.9994654,0.0001840162,0.00003824582,0.0001155323,0.0001266121,0.00007012788],"domain_scores_gemma":[0.9985862,0.0007820642,0.0001311714,0.00008517972,0.0003407468,0.0000747306],"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":[0.0004298283,0.0009932132,0.03255442,0.0001832546,0.0003032113,0.0002540994,0.0001201518,0.1606638,0.01072859,0.002339723,0.004312906,0.7871168],"study_design_scores_gemma":[0.00001673814,0.0003018964,0.007250695,0.0000509458,0.0001060467,0.0001985524,0.00003864606,0.9808728,0.004252169,0.005157377,0.001714437,0.00003980215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2280342,0.00537816,0.7565503,0.001979203,0.0002481281,0.0001851738,0.000450103,0.002329582,0.00484517],"genre_scores_gemma":[0.8870913,0.001093109,0.1090988,0.00030701,0.0002798023,0.00007002093,0.0004836091,0.00003824705,0.001538013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003426323,"threshold_uncertainty_score":0.01208395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02378172481425599,"score_gpt":0.2890402966174187,"score_spread":0.2652585718031627,"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."}}