{"id":"W2537243181","doi":"10.1016/j.jalz.2016.06.1717","title":"P3‐059: Does Synthetic Data Oversampling in Feature Selection Improve the Classification Rate in Alzheimer's Disease?","year":2016,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill Genome Centre","funders":"","keywords":"Undersampling; Oversampling; Feature selection; Bootstrapping (finance); Artificial intelligence; Overfitting; Computer science; Pattern recognition (psychology); Machine learning; Feature (linguistics); Normalization (sociology); Artificial neural network; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01459316,0.0005643898,0.0006592316,0.00040874,0.0003369882,0.0008260171,0.000730084,0.0008281363,0.002064213],"category_scores_gemma":[0.03700303,0.000185182,0.0006640229,0.0003649098,0.0004954807,0.0009499695,0.0006406539,0.0006343524,0.0004970208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003577341,"about_ca_system_score_gemma":0.0006242652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001638201,"about_ca_topic_score_gemma":0.001530795,"domain_scores_codex":[0.9973651,0.001902489,0.0001179706,0.0002887392,0.0002353815,0.00009038049],"domain_scores_gemma":[0.9852975,0.01072502,0.0005509424,0.001791887,0.00138182,0.0002527271],"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":[0.0131563,0.001217435,0.100229,0.0008247598,0.001230265,0.0003755082,0.0004465471,0.2042149,0.01369181,0.004794122,0.02656893,0.6332504],"study_design_scores_gemma":[0.0004143237,0.001943576,0.02183578,0.00009411635,0.0001949067,0.0002923977,0.0001554046,0.9563242,0.009355416,0.00491562,0.004434828,0.00003934632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7660835,0.002286336,0.2198897,0.003374923,0.0006129143,0.0003399368,0.002135069,0.002022396,0.003255215],"genre_scores_gemma":[0.944919,0.0002427411,0.05089256,0.0003376318,0.0001146893,0.0001441274,0.002392807,0.0001202598,0.0008362703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01459316,"threshold_uncertainty_score":0.07717687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1646958124170238,"score_gpt":0.4325401594097006,"score_spread":0.2678443469926768,"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."}}