{"id":"W4390905267","doi":"10.1109/tase.2024.3350894","title":"A Novel Hybrid Ordinal Learning Model With Health Care Application","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; Genentech; IXICO; Banner Alzheimer’s Foundation; Northern California Institute for Research and Education; University of Southern California; Biogen; BioClinica; Meso Scale Diagnostics; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Canadian Institutes of Health Research; National Science Foundation","keywords":"Computer science; HOL; Artificial intelligence; Machine learning; Ordinal regression; Set (abstract data type); Interval (graph theory); Focus (optics); Data mining; Mathematics","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":[0.0006346152,0.0001221112,0.0001221867,0.0003891643,0.00126335,0.00005821745,0.0001008559,0.00004724494,0.00001059514],"category_scores_gemma":[0.00002023078,0.0001079503,0.00001946636,0.0008142578,0.00007769081,0.0005014436,0.000001988425,0.000563159,0.00005742233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005418444,"about_ca_system_score_gemma":0.0008916464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002706168,"about_ca_topic_score_gemma":0.0001149621,"domain_scores_codex":[0.9985375,0.00002194389,0.0003029983,0.0003524738,0.0004082122,0.0003768561],"domain_scores_gemma":[0.9992724,0.0001310099,0.0000496867,0.0001479025,0.0002359478,0.0001630178],"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":[0.00001127548,0.00001349062,0.00002849147,0.0005911512,0.000006018708,9.031822e-7,0.01256428,0.8679378,0.007467878,0.001526519,0.00002594762,0.1098263],"study_design_scores_gemma":[0.00005004918,0.0000749677,0.0000672075,0.0003392945,0.000005447857,0.000008544452,0.00295711,0.9940848,0.001547075,0.00001265735,0.0007423263,0.0001104629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07843252,0.00007708437,0.918972,0.000943005,0.0003275328,0.0004478038,0.0000157227,0.0006352551,0.0001491169],"genre_scores_gemma":[0.9929333,0.00002551188,0.006496718,0.0001845802,0.00004569896,0.0002151764,0.000002616066,0.00002227855,0.00007415837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9145007,"threshold_uncertainty_score":0.9716794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05388135887039508,"score_gpt":0.3954082732373139,"score_spread":0.3415269143669188,"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."}}