{"id":"W4307462465","doi":"10.1038/s41598-022-22979-3","title":"The development of an automated machine learning pipeline for the detection of Alzheimer’s Disease","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Conseil National de la Recherche Scientifique; Université Libanaise; Centre National de la Recherche Scientifique; Agence Universitaire de la Francophonie","keywords":"Computer science; Machine learning; Artificial intelligence; Pipeline (software); Modalities; Dementia; Scalability; Feature selection; Electroencephalography; Automation; Artificial neural network; Disease; Logistic regression; Medicine; Pathology","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":[0.002134132,0.001109967,0.0007477091,0.001187134,0.0004952866,0.00104959,0.001329148,0.001190303,0.004559673],"category_scores_gemma":[0.005345332,0.0006075667,0.0009508165,0.0008883935,0.0003406436,0.001555826,0.001032366,0.001695801,0.004817418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000629776,"about_ca_system_score_gemma":0.001994757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005133369,"about_ca_topic_score_gemma":0.005796844,"domain_scores_codex":[0.9992347,0.0001772379,0.00004591705,0.0002496361,0.0001906846,0.0001018424],"domain_scores_gemma":[0.9984072,0.0005843072,0.0000948435,0.0002229738,0.0006207239,0.0000700567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004852255,0.0003993802,0.008056762,0.0002578226,0.0001980229,0.000364339,0.0001470417,0.07197144,0.06511975,0.005255407,0.02295186,0.824793],"study_design_scores_gemma":[0.00004225169,0.000122191,0.003104031,0.00002938234,0.00003022018,0.0001361283,0.00002979175,0.9579586,0.02518304,0.005836152,0.007494275,0.0000339054],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01406068,0.0002728236,0.9705385,0.0005671316,0.00009224907,0.0001413515,0.0007217868,0.01248715,0.001118321],"genre_scores_gemma":[0.2358634,0.0003632526,0.7545654,0.0004927136,0.0001108147,0.000397101,0.00299192,0.0004531154,0.004762204],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005133369,"threshold_uncertainty_score":0.0152536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03821042919955665,"score_gpt":0.3014426041159664,"score_spread":0.2632321749164097,"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."}}