{"id":"W3145812136","doi":"10.3390/brainsci11040453","title":"Diagnostic Classification and Biomarker Identification of Alzheimer’s Disease with Random Forest Algorithm","year":2021,"lang":"en","type":"article","venue":"Brain Sciences","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Servier; Handong Global University; Eisai; National Research Foundation of Korea; Ministry of Science and ICT, South Korea; BioClinica; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; National Research Foundation; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health; U.S. Department of Defense","keywords":"Random forest; Biomarker; Identification (biology); Disease; Algorithm; Alzheimer's disease; Computer science; Artificial intelligence; Medicine; Pathology; Biology; Ecology","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":[0.0006021695,0.0001031338,0.0001200647,0.0001241447,0.0003143126,0.0001451073,0.0001615015,0.00002939953,0.00003450032],"category_scores_gemma":[0.005011559,0.00008275828,0.0000344031,0.0009531819,0.0009203631,0.0003578624,0.00003255202,0.00005314961,0.00001569721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001143448,"about_ca_system_score_gemma":0.0001315547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005306154,"about_ca_topic_score_gemma":0.00001123898,"domain_scores_codex":[0.9983208,0.0002311605,0.0002995601,0.0005490162,0.0004380744,0.0001613999],"domain_scores_gemma":[0.9980987,0.001140217,0.0002412448,0.0002542797,0.0001502323,0.0001152803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001075075,0.0003178017,0.02224392,0.00004929028,0.00001500537,0.00002836906,0.0003270791,0.000101248,0.8053056,0.02185964,0.001245523,0.148399],"study_design_scores_gemma":[0.0008175909,0.00005517063,0.8067325,0.00004015868,0.00004155198,0.00003421914,0.00043086,0.04474138,0.1434988,0.002215795,0.001204979,0.0001869564],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9411405,0.0007793842,0.04278924,0.01197314,0.0005689697,0.0007405198,0.00005219621,0.0001261105,0.001829946],"genre_scores_gemma":[0.9986838,0.00008321186,0.0005142777,0.0003545974,0.00003122129,0.00005889987,0.00000638503,0.000007144214,0.000260429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7844886,"threshold_uncertainty_score":0.5999666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06333652610116536,"score_gpt":0.3050117454472355,"score_spread":0.2416752193460701,"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."}}