{"id":"W4224257823","doi":"10.1038/s41598-022-10202-2","title":"In-depth insights into Alzheimer’s disease by using explainable machine learning approach","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Horizon 2020; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Horizon 2020 Framework Programme; Genentech; IXICO; H. Lundbeck A/S; Servier; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Javna Agencija za Raziskovalno Dejavnost RS; Eisai; National Institutes of Health; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; European Commission; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Interpretability; Computer science; Preprocessor; Machine learning; Artificial intelligence; Hyperparameter; Cognition; Set (abstract data type); Data science; Redundancy (engineering); Data mining; Cognitive psychology; Psychology; Psychiatry","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":[0.0007357186,0.0006344876,0.0003832615,0.001285802,0.0001945054,0.00104776,0.0005663942,0.0005934708,0.002306143],"category_scores_gemma":[0.002019535,0.0001483921,0.0007720087,0.0005757196,0.0003213342,0.0009782466,0.0006257216,0.0008355225,0.0002569192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005407546,"about_ca_system_score_gemma":0.0005421343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002355509,"about_ca_topic_score_gemma":0.002728378,"domain_scores_codex":[0.9997794,0.0001011733,0.000009793375,0.00004291001,0.00004391394,0.00002288891],"domain_scores_gemma":[0.9992113,0.0005269599,0.00009623865,0.00007074165,0.00006721488,0.00002752082],"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":[0.0002065215,0.0003225229,0.04750159,0.0008253412,0.0006315703,0.001183073,0.001357954,0.545086,0.01093432,0.1183532,0.005062365,0.2685356],"study_design_scores_gemma":[0.00001301554,0.00008816287,0.01042931,0.0001184559,0.0001126442,0.0001769818,0.0002604272,0.8645205,0.001623121,0.1159064,0.006724671,0.00002618925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1887801,0.003649744,0.7930207,0.00390765,0.0001165348,0.0001017268,0.0008538867,0.0006469126,0.008922719],"genre_scores_gemma":[0.8731103,0.002323226,0.1207607,0.000248167,0.0001279767,0.00008250484,0.0007731916,0.0000498277,0.002523988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002355509,"threshold_uncertainty_score":0.007714808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03224795635857101,"score_gpt":0.3105998381705039,"score_spread":0.2783518818119329,"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."}}