{"id":"W1978763244","doi":"10.1016/j.nicl.2013.05.004","title":"Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment","year":2013,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":254,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Engineering and Physical Sciences Research Council; University of California, San Diego; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, Los Angeles; National Institutes of Health; Genentech; Takeda Pharmaceutical Company; IXICO; Servier; Eisai; Northern California Institute for Research and Education; Alzheimer's Disease Neuroimaging Initiative; GE Healthcare; Pfizer; Biogen; BioClinica; Synarc; Medpace; Novartis Pharmaceuticals Corporation; Eli Lilly and Company; Bristol-Myers Squibb; F. Hoffmann-La Roche; Merck; Alzheimer's Drug Discovery Foundation; Meso Scale Diagnostics; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Support vector machine; Machine learning; Probabilistic logic; Computer science; Population; Categorical variable; Probabilistic classification; Cognitive impairment; Disease; Naive Bayes classifier; Medicine; Pattern recognition (psychology); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001409618,0.0004521141,0.0004530188,0.0008887901,0.0001959084,0.0007433295,0.0003309964,0.0007015621,0.0005865006],"category_scores_gemma":[0.009114576,0.0002051004,0.0004284586,0.0003980024,0.000297781,0.0005050354,0.0006552369,0.0005736842,0.0002214259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003235981,"about_ca_system_score_gemma":0.0002772948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007085642,"about_ca_topic_score_gemma":0.004522507,"domain_scores_codex":[0.9996531,0.0001321471,0.00002979894,0.00007750575,0.00005833861,0.00004904173],"domain_scores_gemma":[0.9978611,0.001193016,0.0004213751,0.0001659661,0.0002026215,0.0001560005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000858275,0.00009335515,0.9611781,0.00001490317,0.00008226158,0.0002479884,0.0001653927,0.01606402,0.001081292,0.000178212,0.000416308,0.01962002],"study_design_scores_gemma":[0.00002352597,0.0003106532,0.841904,0.0000120194,0.00006333111,0.0004640573,0.0001976061,0.1542278,0.0008606785,0.001706888,0.0001926255,0.00003687158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964966,0.00007881334,0.002743674,0.00007030585,0.000004250714,0.000008823574,0.0002552308,0.00003737254,0.0003049101],"genre_scores_gemma":[0.9991428,0.00002841122,0.0005398389,0.000008873282,0.000005242841,0.000003551638,0.0002111203,0.000002206857,0.00005795612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007085642,"threshold_uncertainty_score":0.01408881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04313441181511563,"score_gpt":0.3352258579015511,"score_spread":0.2920914460864355,"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."}}