{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003776018,0.001176642,0.001622312,0.003772744,0.0005440909,0.000653571,0.001107222,0.001205366,0.00115053],"category_scores_gemma":[0.005434196,0.0004232317,0.00189274,0.00146616,0.0003188256,0.0008079963,0.0006298168,0.001086144,0.0007341554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005330446,"about_ca_system_score_gemma":0.0014326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009287188,"about_ca_topic_score_gemma":0.006136181,"domain_scores_codex":[0.998708,0.0004333922,0.0001439613,0.0003132554,0.0002307687,0.000170709],"domain_scores_gemma":[0.9985369,0.0007050932,0.0001673429,0.0001029959,0.0004199469,0.00006761988],"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":[0.0007215845,0.0005122449,0.02900095,0.0001909829,0.0004464702,0.0003235503,0.000125387,0.2343586,0.004879629,0.002480076,0.01097741,0.7159832],"study_design_scores_gemma":[0.00004941675,0.00007033603,0.002066123,0.00002047872,0.00006461745,0.0001498644,0.00001849417,0.9930444,0.001052347,0.002821645,0.0006227486,0.00001944504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07044664,0.001772957,0.9215104,0.0004347822,0.0001486391,0.0003005127,0.0006294341,0.0036015,0.001155062],"genre_scores_gemma":[0.5126395,0.0006086722,0.4828064,0.000218379,0.0001909404,0.0004271871,0.001939925,0.0001024975,0.001066492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009287188,"threshold_uncertainty_score":0.0199697,"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."}}