{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001335603,0.0007875668,0.0005365878,0.001934817,0.0003122622,0.0006697199,0.0005501306,0.0007437809,0.0009700523],"category_scores_gemma":[0.004666184,0.0002001615,0.000519023,0.00145367,0.0004005387,0.001045194,0.0005205451,0.0007119835,0.0003015803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005561909,"about_ca_system_score_gemma":0.0003706499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003042839,"about_ca_topic_score_gemma":0.002571395,"domain_scores_codex":[0.9994809,0.0002121214,0.00003212686,0.0001101703,0.0001164824,0.00004818023],"domain_scores_gemma":[0.9987339,0.0007515048,0.0002215481,0.00008812013,0.0001644366,0.00004044935],"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":[0.0004502552,0.000276622,0.02091417,0.0002683878,0.0002544937,0.0002745086,0.0001397563,0.4210269,0.01270823,0.01311523,0.002930581,0.5276409],"study_design_scores_gemma":[0.000007589169,0.0000668471,0.00489846,0.00001604681,0.0000243608,0.00009211116,0.00002069984,0.9841477,0.002277425,0.007778791,0.000659041,0.00001096771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2425734,0.004269362,0.7479529,0.001025503,0.0001073472,0.000111741,0.000689141,0.0007018681,0.002568799],"genre_scores_gemma":[0.885792,0.00154814,0.1101486,0.00009412692,0.0001415633,0.0001107446,0.0007316765,0.00003651192,0.001396612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003042839,"threshold_uncertainty_score":0.007063448,"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."}}