{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001630798,0.0001490042,0.0003633734,0.0001865753,0.0001936613,0.00006428701,0.001159006,0.00009646841,0.000002848588],"category_scores_gemma":[0.0007287191,0.0000990016,0.00007176567,0.0005879005,0.00005110556,0.0003871394,0.002300991,0.0002929231,0.000001021648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001340649,"about_ca_system_score_gemma":0.0000509796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004258203,"about_ca_topic_score_gemma":0.000004943702,"domain_scores_codex":[0.9970857,0.0005196076,0.0007500729,0.0009332321,0.0004108172,0.0003005798],"domain_scores_gemma":[0.9976231,0.0006440315,0.0003857918,0.001105191,0.0001007606,0.0001410599],"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":[0.00001039604,0.00003834604,0.2704132,0.00004861522,0.00006870814,0.000008001289,0.0001682914,0.003314777,0.001952713,0.00007512951,0.00009103135,0.7238108],"study_design_scores_gemma":[0.0002501213,0.00006862544,0.007719414,0.0002607806,0.00005974475,0.000006053738,0.000005166777,0.991268,0.00008336485,0.00002815822,0.0001495469,0.0001010673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5215698,0.0001965398,0.4770711,0.0006944639,0.0001951413,0.0001044834,0.000007392611,0.0001414517,0.00001955695],"genre_scores_gemma":[0.9588296,0.00004780047,0.04091017,0.00008720459,0.00009056475,0.000001417712,0.00001239248,0.0000125259,0.000008266521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9879532,"threshold_uncertainty_score":0.4037168,"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."}}