{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003187682,0.0001768607,0.0001529336,0.0001833521,0.0003558415,0.00007264085,0.0001228903,0.00007954164,0.0002430273],"category_scores_gemma":[0.0001551087,0.0001553627,0.000102975,0.0002017134,0.00018514,0.0003399501,0.000002202261,0.0003551581,0.0000989718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000669746,"about_ca_system_score_gemma":0.00008233229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004722818,"about_ca_topic_score_gemma":0.000002955862,"domain_scores_codex":[0.9981139,0.0001205072,0.0003233535,0.0006509437,0.0005373433,0.0002539682],"domain_scores_gemma":[0.9989418,0.0002312257,0.0001101113,0.0002897708,0.00005572132,0.0003713034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000648503,0.0009599343,0.001952801,0.0001320283,0.00002645882,0.00001786126,0.0002824229,0.0003988285,0.5090796,0.0008646271,0.0008062873,0.4848306],"study_design_scores_gemma":[0.001957525,0.00007789278,0.027421,0.0001775974,0.00008564704,0.00006119377,0.0001387338,0.8791211,0.08900819,0.0001521508,0.001553259,0.0002457728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08099957,0.00006242708,0.9095528,0.00616734,0.001836527,0.000834589,0.00005652502,0.0003078753,0.0001823742],"genre_scores_gemma":[0.9980978,0.00009059655,0.0003725757,0.0007670924,0.00008617001,0.0001887997,0.000006546759,0.00002925883,0.0003611587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9170982,"threshold_uncertainty_score":0.6335508,"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."}}