{"id":"W2519105757","doi":"10.1109/jbhi.2016.2608998","title":"A Nonparametric Approach for Mild Cognitive Impairment to AD Conversion Prediction: Results on Longitudinal Data","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; Janssen Research and Development; National Institutes of Health; H. Lundbeck A/S; Servier; Novartis Pharmaceuticals Corporation; GE Healthcare; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Biogen; Takeda Pharmaceutical Company; Bristol-Myers Squibb; Eli Lilly and Company; Alzheimer's Drug Discovery Foundation; Merck; National Institute on Aging; Fujirebio Europe","keywords":"Computer science; Nonparametric statistics; Cognition; Cognitive impairment; Artificial intelligence; Longitudinal data; Machine learning; Pattern recognition (psychology); Data mining; Econometrics; Psychology; Mathematics; Neuroscience","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":[0.0104221,0.0008059992,0.0006706473,0.0009702149,0.0004206644,0.0008923133,0.0006939476,0.0007863651,0.0009507223],"category_scores_gemma":[0.01810667,0.0001547082,0.0009062863,0.0006318023,0.0003788596,0.0009200997,0.0009896848,0.00115828,0.0004121405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003409128,"about_ca_system_score_gemma":0.0008725966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007256633,"about_ca_topic_score_gemma":0.004342145,"domain_scores_codex":[0.9974111,0.001640727,0.0001258229,0.0003908967,0.000308867,0.0001226401],"domain_scores_gemma":[0.9908975,0.006245206,0.000467329,0.00110569,0.001006136,0.000278114],"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":[0.003352819,0.001610838,0.2840716,0.0003807018,0.0008943176,0.0004988973,0.0004457427,0.1975169,0.003604485,0.001752318,0.006977767,0.4988936],"study_design_scores_gemma":[0.00006320184,0.0008850053,0.06201218,0.00007982515,0.0001458623,0.0002288981,0.0004196103,0.9298855,0.002117568,0.002330138,0.001761442,0.00007076084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8785152,0.003969514,0.1113923,0.0009285033,0.000196973,0.000168471,0.001592956,0.001013045,0.002223012],"genre_scores_gemma":[0.9694443,0.0006804022,0.02605175,0.0000915194,0.00008232902,0.00009098611,0.002399922,0.0000528102,0.001106044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0104221,"threshold_uncertainty_score":0.05511796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1735163544042974,"score_gpt":0.3627308389052829,"score_spread":0.1892144845009855,"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."}}