{"id":"W2286635690","doi":"10.1016/j.neucom.2015.07.145","title":"Modeling and predicting AD progression by regression analysis of sequential clinical data","year":2016,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Janssen Research and Development; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; U.S. Department of Defense; Eli Lilly and Company; Lundbeckfonden; Northern California Institute for Research and Education; Alzheimer's Disease Neuroimaging Initiative; GE Healthcare; Pfizer; BioClinica; Biogen; Novartis Pharmaceuticals Corporation; King Abdullah University of Science and Technology; Bristol-Myers Squibb; F. Hoffmann-La Roche; Merck; Alzheimer's Drug Discovery Foundation; Meso Scale Diagnostics; Johnson and Johnson; Takeda Pharmaceutical Company; AbbVie; Fujirebio Europe; Alzheimer's Association","keywords":"Computer science; Regression analysis; Regression; Artificial intelligence; Machine learning; Statistics; Mathematics","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":[0.003374967,0.001103656,0.001373259,0.001874777,0.0002188023,0.001024307,0.0009058327,0.0007422327,0.001362231],"category_scores_gemma":[0.009303625,0.0004920986,0.001137209,0.001366053,0.0003160193,0.0008353124,0.0004704227,0.001640565,0.0007157179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003912773,"about_ca_system_score_gemma":0.00093088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009293755,"about_ca_topic_score_gemma":0.007742059,"domain_scores_codex":[0.999084,0.0004012194,0.00007797393,0.0002352227,0.0001107548,0.00009080501],"domain_scores_gemma":[0.9949522,0.003682239,0.00057327,0.0003073173,0.0003571647,0.0001279316],"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":[0.002301733,0.001323528,0.2805784,0.0003626334,0.0009124788,0.0007804749,0.0002699161,0.4586293,0.007217744,0.003416512,0.004167674,0.2400396],"study_design_scores_gemma":[0.00003673373,0.0002984633,0.01554814,0.00002019973,0.0001197143,0.0002392026,0.00003468725,0.9784068,0.001047387,0.003653587,0.0005757444,0.00001927983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7178009,0.003090153,0.2722542,0.00133176,0.0001601551,0.000175919,0.003050711,0.0009576812,0.001178433],"genre_scores_gemma":[0.971203,0.0008607471,0.02449387,0.00007379381,0.000111594,0.000113653,0.001762497,0.0000383423,0.001342401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009293755,"threshold_uncertainty_score":0.01847935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.092810977367882,"score_gpt":0.4218802063523335,"score_spread":0.3290692289844515,"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."}}