{"id":"W3193635273","doi":"10.25148/etd.fidc009203","title":"A Multimodal Neuroimaging Approach for Classification and Prediction of Alzheimer's Disease Using Machine Learning","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"NeuroRx Research (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; Servier; Eisai; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; Foundation for the National Institutes of Health; National Institute on Aging; Alzheimer's Association; Canadian Institutes of Health Research; National Science Foundation","keywords":"Machine learning; Artificial intelligence; Dementia; Support vector machine; Neuroimaging; Feature selection; Disease; Cognitive decline; Population; Computer science; Positron emission tomography; Deep learning; Medicine; Psychology; Pathology; Neuroscience","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007416811,0.0007764524,0.0008600812,0.001253142,0.0003597228,0.0009217045,0.0009745238,0.001167262,0.001278329],"category_scores_gemma":[0.002031025,0.00022944,0.0008986703,0.0009574722,0.0002899653,0.000815619,0.0006716324,0.000929687,0.0004653605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005699291,"about_ca_system_score_gemma":0.0008546301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006889135,"about_ca_topic_score_gemma":0.005275398,"domain_scores_codex":[0.9997085,0.00007280562,0.00001859961,0.00008372662,0.00006892519,0.0000474733],"domain_scores_gemma":[0.9996871,0.0001127232,0.00003783189,0.00002560264,0.0001143146,0.00002250492],"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":[0.0002854873,0.0003340055,0.009231995,0.0001646685,0.0002604522,0.0005478682,0.0001462098,0.2039794,0.01323752,0.006401089,0.00673322,0.758678],"study_design_scores_gemma":[0.000005308854,0.00005890019,0.001500006,0.00001634962,0.00002911742,0.00008616484,0.00002702334,0.9924516,0.001538187,0.003469509,0.0008057419,0.0000120875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04290269,0.00204823,0.9504895,0.001066051,0.0001350603,0.000117744,0.0003701753,0.0008294487,0.002041056],"genre_scores_gemma":[0.694557,0.001755134,0.2977622,0.000409493,0.0003728445,0.0003449067,0.0007350871,0.00007047206,0.003992889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006889135,"threshold_uncertainty_score":0.01369804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06680226804493652,"score_gpt":0.348134983533537,"score_spread":0.2813327154886005,"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."}}