{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003036915,0.0008941678,0.001400158,0.001020858,0.0004518041,0.001808916,0.003230526,0.002011403,0.00475776],"category_scores_gemma":[0.006865127,0.0004513235,0.0009974712,0.001248361,0.0007872326,0.002538536,0.002034871,0.002471734,0.001002516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001207225,"about_ca_system_score_gemma":0.001231968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004773235,"about_ca_topic_score_gemma":0.004019694,"domain_scores_codex":[0.9985165,0.0006034713,0.00008600199,0.0003627716,0.0002642664,0.0001669979],"domain_scores_gemma":[0.9973316,0.00160818,0.0002553731,0.000129192,0.0005146571,0.000160936],"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":[0.0003908593,0.0003566907,0.01065075,0.0002485908,0.0001325824,0.0003355152,0.0002156619,0.8018633,0.0009846408,0.0310393,0.006816665,0.1469655],"study_design_scores_gemma":[0.00001235905,0.00002840117,0.0001846774,0.000009832016,0.0000101955,0.00002245439,0.00001008951,0.9934288,0.0001043466,0.005591443,0.0005908689,0.000006550222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02927716,0.0009667054,0.9638872,0.001508621,0.00009443391,0.0001172001,0.0005830627,0.0006021436,0.002963509],"genre_scores_gemma":[0.7801937,0.000808154,0.2033917,0.001335017,0.0002604043,0.0006573144,0.001463002,0.0001246371,0.01176606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004773235,"threshold_uncertainty_score":0.01606095,"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."}}