{"id":"W2535391591","doi":"10.1016/j.patrec.2016.10.010","title":"Combining multiple approaches for the early diagnosis of Alzheimer's Disease","year":2016,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; National Institutes of Health; Canadian Institutes of Health Research; Centre National de la Recherche Scientifique; Consiglio Nazionale delle Ricerche; U.S. Department of Defense","keywords":"Support vector machine; Artificial intelligence; Computer science; Pattern recognition (psychology); Curse of dimensionality; Feature (linguistics); Set (abstract data type); Machine learning; Dimensionality reduction; Feature vector","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":[0.0001597728,0.0001073243,0.00008710226,0.00002747911,0.00007055012,0.00001507139,0.000139721,0.00004118777,0.00002377476],"category_scores_gemma":[0.0002237392,0.00006687734,0.0001142158,0.00002711124,0.00008931474,0.000006987336,0.00005166973,0.00004195143,0.000015671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004231167,"about_ca_system_score_gemma":0.000008874495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001457149,"about_ca_topic_score_gemma":0.00000427708,"domain_scores_codex":[0.9993468,0.00004482223,0.000205356,0.0001527937,0.00009503389,0.0001552094],"domain_scores_gemma":[0.9993343,0.0001993898,0.0001538379,0.0002239248,0.00003869852,0.00004985411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001312378,0.0000651413,0.4827544,0.00006591493,0.0002460941,5.612534e-7,0.0001634657,0.0000706671,0.016008,0.000003400294,0.003112786,0.4973783],"study_design_scores_gemma":[0.008281297,0.0007249173,0.6235592,0.0005060657,0.0008600872,0.000009730847,0.0002264093,0.007163972,0.3322996,0.0001918165,0.02483784,0.00133909],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8603821,0.0001203151,0.1349687,0.003733885,0.0001345085,0.0004082517,0.0002040107,0.00001389187,0.00003431471],"genre_scores_gemma":[0.9967815,0.00003895807,0.0009775634,0.001563864,0.0001462087,0.000298659,0.0001645349,0.00002022148,0.000008550309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4960392,"threshold_uncertainty_score":0.2727179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04312735340193236,"score_gpt":0.248125077360002,"score_spread":0.2049977239580696,"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."}}