{"id":"W4366166011","doi":"10.1016/j.artmed.2023.102551","title":"Term dependency extraction using rule-based Bayesian Network for medical image retrieval","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Bayesian network; Association rule learning; Image retrieval; Data mining; Information retrieval; Artificial intelligence; Set (abstract data type); Relevance (law); Image (mathematics); Machine learning; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001361541,0.0008857732,0.001326609,0.004381263,0.0007169064,0.001270615,0.001381963,0.001330858,0.002751304],"category_scores_gemma":[0.005534516,0.0005063028,0.00153166,0.002646727,0.0003395657,0.001562147,0.0006567668,0.001266921,0.001943378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008273756,"about_ca_system_score_gemma":0.001902413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01259479,"about_ca_topic_score_gemma":0.01342508,"domain_scores_codex":[0.9987097,0.0001969655,0.0001679236,0.0003214314,0.0005092367,0.00009486264],"domain_scores_gemma":[0.998214,0.0009240178,0.0001477737,0.0001161047,0.000560144,0.00003793934],"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":[0.000533774,0.0004728949,0.006505652,0.0005055455,0.0003354865,0.0005502942,0.0001188386,0.08802301,0.0257061,0.005455412,0.009008957,0.8627841],"study_design_scores_gemma":[0.00003455883,0.00009357878,0.003070874,0.00007136996,0.0003246194,0.000379464,0.00002311857,0.9743256,0.009702479,0.007657665,0.004263106,0.00005345659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02180639,0.002448642,0.968915,0.0003504787,0.0001175025,0.000289593,0.001144666,0.001975857,0.002951944],"genre_scores_gemma":[0.4360128,0.002925968,0.5476567,0.0004457993,0.0003290511,0.000599664,0.004965824,0.000232235,0.006831868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01259479,"threshold_uncertainty_score":0.02504295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07771730144249658,"score_gpt":0.3919361569501257,"score_spread":0.3142188555076292,"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."}}