{"id":"W2996343685","doi":"10.1109/tmi.2019.2958943","title":"Joint Multi-Modal Longitudinal Regression and Classification for Alzheimer’s Disease Prediction","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Science Foundation of Sri Lanka; Canadian Institutes of Health Research; National Institute on Aging; National Institutes of Health","keywords":"Joint (building); Regression; Artificial intelligence; Modal; Pattern recognition (psychology); Computer science; Regression analysis; Machine learning; Statistics; Mathematics; Engineering","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":[0.007201029,0.001301861,0.001560851,0.001429451,0.0008330759,0.001080111,0.002335467,0.002044783,0.002725562],"category_scores_gemma":[0.01325765,0.0007187476,0.002333955,0.001468652,0.0006849017,0.001266037,0.001731965,0.003631803,0.001564801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009811887,"about_ca_system_score_gemma":0.001748264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01197768,"about_ca_topic_score_gemma":0.01176512,"domain_scores_codex":[0.9981596,0.0009025719,0.0000964229,0.0003988818,0.0002603828,0.0001822791],"domain_scores_gemma":[0.994907,0.002997447,0.0005459056,0.0005561202,0.0007739554,0.0002195695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006233564,0.0005435347,0.01890291,0.00021763,0.0005195389,0.0002025428,0.0001948711,0.5887241,0.004201559,0.01413103,0.01414483,0.3575941],"study_design_scores_gemma":[0.000008069312,0.00001599619,0.000538708,0.000008457904,0.00001254151,0.00001835097,0.000008967034,0.9948286,0.0002983977,0.003747609,0.0005033871,0.00001085483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01184456,0.0008019797,0.984601,0.0007011107,0.00007545784,0.00005060331,0.0002672489,0.001269054,0.000389092],"genre_scores_gemma":[0.3933788,0.001026793,0.5953295,0.0008404786,0.0005561662,0.0005194489,0.002526322,0.0004230289,0.005399472],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01197768,"threshold_uncertainty_score":0.03808314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08525684057052643,"score_gpt":0.3239362792550483,"score_spread":0.2386794386845219,"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."}}