{"id":"W2972028780","doi":"10.1101/755058","title":"Predicting Alzheimer’s disease progression using deep recurrent neural networks","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital; Genentech; National Institutes of Health; National Supercomputing Centre Singapore; National Research Foundation Singapore; IXICO; H. Lundbeck A/S; Servier; Eisai; National Research Foundation; Centre d'Imagerie BioMédicale; Pfizer; Biogen; BioClinica; Nvidia; F. Hoffmann-La Roche; University of Southern California; Northern California Institute for Research and Education; Massachusetts General Hospital; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Missing data; Dementia; Artificial intelligence; Machine learning; Computer science; Disease; Recurrent neural network; Alzheimer's Disease Neuroimaging Initiative; Neuroimaging; Artificial neural network; Psychology; Medicine; Neuroscience; 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":[0.001902483,0.0009098666,0.0007882103,0.0007825505,0.0002342941,0.0006472638,0.0009647148,0.0006283985,0.0007993288],"category_scores_gemma":[0.004454732,0.0003200391,0.0007551102,0.000462582,0.0002217058,0.0009201299,0.0007118691,0.001495719,0.0003560864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006424764,"about_ca_system_score_gemma":0.0005823714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01249293,"about_ca_topic_score_gemma":0.01893493,"domain_scores_codex":[0.9995916,0.0001187349,0.00003180719,0.0001352136,0.00005702392,0.00006550006],"domain_scores_gemma":[0.9986952,0.000557085,0.0002121514,0.0001261404,0.0003165089,0.00009284072],"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":[0.00112796,0.0006762589,0.1579605,0.0002153226,0.0006403051,0.0004675485,0.0001884946,0.6106489,0.004676656,0.001832016,0.008396657,0.2131694],"study_design_scores_gemma":[0.000009998711,0.00006532276,0.004733448,0.00001787038,0.00003454446,0.0000268713,0.00001282996,0.9922677,0.0006596208,0.001907457,0.0002533861,0.00001100674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8460546,0.004419368,0.140648,0.001746608,0.0002353504,0.00006613158,0.003663816,0.001306296,0.001859887],"genre_scores_gemma":[0.9877002,0.0002760545,0.009537145,0.000103105,0.00004590937,0.00001984954,0.00176566,0.00001333646,0.000538628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01249293,"threshold_uncertainty_score":0.02484041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03137077317071266,"score_gpt":0.3064830547063844,"score_spread":0.2751122815356717,"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."}}