{"id":"W3003644065","doi":"10.3390/app10030934","title":"Machine Learning and DWI Brain Communicability Networks for Alzheimer’s Disease Detection","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; H. Lundbeck A/S; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Servier; University of Southern California; Eli Lilly and Company; Genentech; IXICO","keywords":"Computer science; Artificial intelligence; Machine learning; Brain disease; Feature (linguistics); Pattern recognition (psychology); Disease; Medicine; Pathology","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":[0.0002047791,0.00006988455,0.0001044215,0.00001675319,0.0003820919,0.00002196148,0.00009311508,0.00001836951,0.000004519982],"category_scores_gemma":[0.00009929903,0.00005885792,0.00002603919,0.0002000318,0.0002940074,0.00003808014,0.00007303825,0.0001474701,8.612123e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005450795,"about_ca_system_score_gemma":0.00001565841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004602627,"about_ca_topic_score_gemma":0.000001898487,"domain_scores_codex":[0.9994118,0.00001445761,0.0001028547,0.0002616874,0.00008183248,0.0001273423],"domain_scores_gemma":[0.9994872,0.0001934326,0.00004436809,0.0001152653,0.00001566024,0.0001441263],"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":[0.001165498,0.0003318927,0.06821449,0.0001920868,0.0000563901,0.00000401501,0.0006989728,0.004812099,0.09519912,0.05899499,0.001575935,0.7687545],"study_design_scores_gemma":[0.001016211,0.0005727051,0.01284266,0.0000227585,0.0001516958,0.00000816854,0.0001875797,0.8484577,0.005739991,0.01228592,0.1183972,0.000317389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09685501,0.002664898,0.8154617,0.07779521,0.00004177324,0.003021727,0.00001690368,0.00105144,0.003091333],"genre_scores_gemma":[0.9843688,0.00006887651,0.0130536,0.002324838,0.00004566025,0.0001149097,0.000007782127,0.000007316429,0.000008237169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8875138,"threshold_uncertainty_score":0.293878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.118911752969959,"score_gpt":0.3755350759972443,"score_spread":0.2566233230272853,"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."}}