{"id":"W1991696758","doi":"10.1016/j.aml.2007.05.010","title":"Integrating rough set theory and medical applications","year":2007,"lang":"en","type":"article","venue":"Applied Mathematics Letters","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Rough set; Set (abstract data type); Perspective (graphical); Set theory; Medical science; Management science; Medical research; Computer science; Mathematics; Data mining; Artificial intelligence; Medicine; Engineering","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":[0.005293352,0.0006347446,0.001962292,0.003107476,0.0004282805,0.004479328,0.00107981,0.001722512,0.002324944],"category_scores_gemma":[0.01279041,0.0003528859,0.001092437,0.002005009,0.002490689,0.003954635,0.002037368,0.002373169,0.0005191183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001198065,"about_ca_system_score_gemma":0.001675267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000968882,"about_ca_topic_score_gemma":0.0009281258,"domain_scores_codex":[0.9971009,0.001534746,0.0001918481,0.0001645422,0.0009034264,0.000104517],"domain_scores_gemma":[0.9957836,0.002781492,0.0002752676,0.0003713698,0.0006565775,0.0001318021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007560624,0.000104237,0.001913843,0.001136031,0.0003417606,0.0005191517,0.0005048679,0.01267046,0.001169338,0.6971442,0.00613416,0.2782863],"study_design_scores_gemma":[0.00001624787,0.0000677446,0.001094054,0.0001822733,0.00008622487,0.0004383933,0.0002299801,0.01594183,0.0003794383,0.9584267,0.02310102,0.0000360482],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01540499,0.1333819,0.7810974,0.02619892,0.002308052,0.0001015623,0.000144153,0.0002788624,0.04108412],"genre_scores_gemma":[0.5404659,0.1003427,0.3405569,0.004389833,0.005864044,0.0001921448,0.0002077373,0.00006923787,0.007911471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005293352,"threshold_uncertainty_score":0.02799428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213794086541275,"score_gpt":0.2533562193093529,"score_spread":0.2412182784439402,"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."}}