{"id":"W1979024706","doi":"10.1016/j.neuroimage.2014.04.018","title":"Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects","year":2014,"lang":"en","type":"article","venue":"NeuroImage","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Mental Health; National Institute on Aging; University of California, San Diego; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, Los Angeles; Genentech; National Institutes of Health; Servier; Eisai; Bundesministerium für Bildung und Forschung; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Alzheimer's Association; Amorfix Life Sciences; Deutscher Akademischer Austauschdienst; National Center for Research Resources; F. Hoffmann-La Roche; Medpace; AstraZeneca; Eli Lilly and Company; Bristol-Myers Squibb; Novartis Pharmaceuticals Corporation; Wellcome Trust; Synarc; Bayer HealthCare; Alzheimer's Disease Neuroimaging Initiative; Medical Research Council; Meso Scale Diagnostics; Foundation for the National Institutes of Health","keywords":"Normative; Inference; Dementia; Psychology; Disease; Artificial intelligence; Medicine; Machine learning; Computer science; 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":[0.002810646,0.0004326851,0.0007462256,0.0005997576,0.0002501236,0.0004670343,0.0007438911,0.0009349777,0.0005628131],"category_scores_gemma":[0.006435029,0.0003136888,0.0005140018,0.0003153353,0.000638125,0.0005309041,0.0006455013,0.0009910304,0.0001460982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005790405,"about_ca_system_score_gemma":0.0007008688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01245668,"about_ca_topic_score_gemma":0.01193415,"domain_scores_codex":[0.9995375,0.000219023,0.00001969501,0.0001215117,0.00004899403,0.00005333123],"domain_scores_gemma":[0.998276,0.001286071,0.0001340289,0.0001175565,0.0001225018,0.00006387295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003273768,0.0001440092,0.03038946,0.00002476555,0.0001031116,0.0001105922,0.000176268,0.8893405,0.001783301,0.002913483,0.0006949937,0.0739921],"study_design_scores_gemma":[0.00001280656,0.00003751955,0.004172055,0.000003927044,0.00001173654,0.00002004396,0.000009531735,0.9925234,0.0003703827,0.002783888,0.00004702184,0.000007818884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7169951,0.0001631407,0.2812951,0.0003321302,0.00001348891,0.00005414392,0.0001289901,0.0004370099,0.0005807726],"genre_scores_gemma":[0.9782168,0.00004425643,0.02104159,0.00006258914,0.00001029296,0.00003199278,0.0001775993,0.0000129904,0.0004019755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01245668,"threshold_uncertainty_score":0.02476835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006741152433292742,"score_gpt":0.2278331672859616,"score_spread":0.2210920148526689,"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."}}