{"id":"W2886568032","doi":"10.1371/journal.pone.0211558","title":"Random forest prediction of Alzheimer’s disease using pairwise selection from time series data","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":135,"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; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Meso Scale Diagnostics; Medical Research Council; Pfizer; Biogen; Eli Lilly and Company; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Engineering and Physical Sciences Research Council; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Random forest; Support vector machine; Computer science; Benchmark (surveying); Time series; Artificial intelligence; Pairwise comparison; Missing data; Alzheimer's Disease Neuroimaging Initiative; Statistics; Machine learning; Alzheimer's disease; Pattern recognition (psychology); Data mining; Disease; Mathematics; Medicine; 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":[0.003594588,0.001024588,0.00101751,0.001360869,0.0003566344,0.0004908976,0.0006464488,0.0006815883,0.001055226],"category_scores_gemma":[0.005254572,0.0002094075,0.001254999,0.0008129532,0.0001997085,0.0006247292,0.0003293781,0.0009784438,0.0006631191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003528711,"about_ca_system_score_gemma":0.0006794562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008614684,"about_ca_topic_score_gemma":0.0098619,"domain_scores_codex":[0.9993649,0.0003153393,0.00002992478,0.0001381465,0.00006821109,0.00008339714],"domain_scores_gemma":[0.996451,0.00247962,0.0002432955,0.000180655,0.000493666,0.0001517521],"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":[0.001202559,0.0004413249,0.06677776,0.0001526248,0.0004979896,0.000401871,0.00008336189,0.6629681,0.002798126,0.001182269,0.009536838,0.2539573],"study_design_scores_gemma":[0.00003220765,0.0001292645,0.005143599,0.00001607014,0.00003447625,0.00008271968,0.00001738048,0.9916467,0.0006253637,0.001862293,0.0003969568,0.00001303329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6088079,0.003521147,0.3784348,0.0010281,0.0004311056,0.0001952593,0.002805235,0.002717151,0.002059396],"genre_scores_gemma":[0.9360663,0.0004137328,0.05757449,0.0001119162,0.0001969594,0.00008563732,0.004412444,0.00007447528,0.0010641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008614684,"threshold_uncertainty_score":0.01901025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0879942957480051,"score_gpt":0.2930750837700457,"score_spread":0.2050807880220406,"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."}}