{"id":"W3017890102","doi":"10.1109/jbhi.2020.2984355","title":"Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":120,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 Marie Skłodowska-Curie Actions; Janssen Alzheimer Immunotherapy Research And Development; Johnson and Johnson Pharmaceutical Research and Development; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Novartis Pharmaceuticals Corporation; Biogen; BioClinica; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; University of Southern California; Engineering and Physical Sciences Research Council; Bristol-Myers Squibb; F. Hoffmann-La Roche; Alzheimer's Drug Discovery Foundation; Alzheimer's Association; Horizon 2020 Framework Programme; Foundation for the National Institutes of Health","keywords":"Disease; Biomarker; Support vector machine; Feature selection; Computer science; Amyloid beta; Machine learning; Alzheimer's disease; Apolipoprotein E; Medicine; Artificial intelligence; Bioinformatics; Computational biology; Pathology; Biology","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":[0.0002667348,0.0001296292,0.0004256089,0.0001543723,0.00008988887,0.00001562163,0.00005654908,0.0000516059,0.00002276125],"category_scores_gemma":[0.00004234243,0.00007894047,0.00007966463,0.0002168081,0.0001766421,0.0002041028,0.00001847955,0.0002653899,0.00000305486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002743764,"about_ca_system_score_gemma":0.0012199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003698224,"about_ca_topic_score_gemma":0.000001306082,"domain_scores_codex":[0.9980124,0.00002888474,0.0008826859,0.00005995099,0.0007610333,0.0002551024],"domain_scores_gemma":[0.9971604,0.00002071816,0.0006197956,0.00007826692,0.000185141,0.001935723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.05479516,0.01526795,0.3845439,0.03814815,0.07999835,0.006908113,0.03296099,0.0001051399,0.006282891,0.0001292725,0.003644165,0.3772159],"study_design_scores_gemma":[0.08602904,0.1661485,0.5851532,0.008204273,0.01800706,0.007818324,0.002539199,0.09596049,0.0235733,0.0002107618,0.004850131,0.001505722],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930843,0.00143839,0.001345264,0.00355145,0.00006309577,0.0004373524,0.00005519054,0.00001095833,0.00001406808],"genre_scores_gemma":[0.9954757,0.00030682,0.003398968,0.0005805237,0.0002156344,0.000002783153,0.000007867711,0.00001093705,7.820103e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3757102,"threshold_uncertainty_score":0.3219099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05468688520294072,"score_gpt":0.3336398252890928,"score_spread":0.278952940086152,"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."}}