{"id":"W4382631688","doi":"10.1093/bioinformatics/btad249","title":"PPAD: a deep learning architecture to predict progression of Alzheimer’s disease","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; National Institute of General Medical Sciences; Northern California Institute for Research and Education; University of North Texas; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Autoencoder; Dementia; Recurrent neural network; Disease; Alzheimer's disease; Neuroimaging; Artificial intelligence; Deep learning; Cognitive impairment; Electronic health record; Medicine; Computer science; Machine learning; Gerontology; Artificial neural network; Psychiatry; Health care; Internal medicine","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":[0.0004303173,0.0001419259,0.0001753288,0.0003062871,0.0001456315,0.00006553155,0.0006569619,0.00005685033,0.000008831934],"category_scores_gemma":[0.0004839922,0.0001169514,0.00006697263,0.000975784,0.00002921472,0.0002117658,0.0005529089,0.0002882346,0.0001993503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001817033,"about_ca_system_score_gemma":0.000100607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008619788,"about_ca_topic_score_gemma":0.000002014769,"domain_scores_codex":[0.9984337,0.00007918651,0.0004279311,0.0001667779,0.0005329319,0.0003594664],"domain_scores_gemma":[0.9987467,0.0001077772,0.0002009864,0.0005273797,0.00008968899,0.000327483],"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":[0.00002359052,0.00002824453,0.02100615,0.0006235869,0.00002500252,0.00001829486,0.01304353,0.05656138,0.00002106668,0.002197075,0.001612278,0.9048398],"study_design_scores_gemma":[0.0001497666,0.0002315402,0.03448543,0.0001994234,0.00001055886,0.000005189156,0.0001149414,0.9545472,0.00007082961,0.0002948055,0.009727485,0.0001628021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07169878,0.0005975029,0.9096082,0.008729922,0.00102017,0.001944532,0.00002415359,0.003022167,0.003354527],"genre_scores_gemma":[0.6745598,0.00003121921,0.324399,0.00057456,0.0001213235,0.00007661732,0.00005404983,0.00003019958,0.0001531893],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.904677,"threshold_uncertainty_score":0.4769138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02247876156206442,"score_gpt":0.3119453816675675,"score_spread":0.2894666201055031,"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."}}