{"id":"W3185071634","doi":"10.1007/978-3-030-87094-2_40","title":"Assessing Significance of Cognitive Assessments for Diagnosing Alzheimer’s Disease with Fuzzy-Rough Feature Selection","year":2021,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; Genentech; IXICO; National Natural Science Foundation of China; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Feature selection; Cognition; Modalities; Computer science; Fuzzy cognitive map; Machine learning; Selection (genetic algorithm); Artificial intelligence; Benchmark (surveying); Feature (linguistics); Exploit; Dementia; Similarity (geometry); Variety (cybernetics); Neuroimaging; Fuzzy logic; Psychology; Disease; Fuzzy set; Medicine; Fuzzy classification; Neuroscience","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003515731,0.0004318842,0.0007076963,0.0001676823,0.000218152,0.0004347153,0.0003187599,0.0001644135,0.000001451525],"category_scores_gemma":[0.00004615028,0.0003749218,0.0001091996,0.0001669223,0.00009717685,0.0007466255,0.0001727719,0.0003566437,5.834275e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008980599,"about_ca_system_score_gemma":0.0001794687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002623033,"about_ca_topic_score_gemma":0.00001851484,"domain_scores_codex":[0.9976414,0.00007597094,0.0006099246,0.0009242872,0.0003874298,0.0003609576],"domain_scores_gemma":[0.9974289,0.001027436,0.0008197694,0.0002621725,0.0003493902,0.0001123654],"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":[0.0001153472,0.000176865,0.004604876,0.001781001,0.0003765801,0.0001610611,0.0006905529,0.02702663,0.000007861513,0.3010365,0.00006353926,0.6639592],"study_design_scores_gemma":[0.003233155,0.002493601,0.003242322,0.09848633,0.001247118,0.0003068701,0.003514956,0.7843009,0.000588229,0.03848027,0.05853462,0.005571587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004453982,0.07077514,0.9077041,0.00004550514,0.001089334,0.001381735,0.00002571701,0.00007172119,0.01846132],"genre_scores_gemma":[0.9504468,0.003149656,0.04392879,0.0001038334,0.0004950241,0.0001071458,0.00008487775,0.00008073977,0.001603147],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9500014,"threshold_uncertainty_score":0.9998703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04592623772835967,"score_gpt":0.3345789044666954,"score_spread":0.2886526667383357,"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."}}