{"id":"W4360989112","doi":"10.18280/ria.370111","title":"An Improved CHI2 Feature Selection Based a Two-Stage Prediction of Comorbid Cancer Patient Survivability","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Survivability; Stage (stratigraphy); Feature selection; Selection (genetic algorithm); Cancer; Feature (linguistics); Artificial intelligence; Computer science; Pattern recognition (psychology); Medicine; Internal medicine; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001738153,0.0003210421,0.0005019965,0.0003309809,0.0009880079,0.00001939433,0.0004065519,0.0003979864,0.001896524],"category_scores_gemma":[0.0006278942,0.0003168946,0.0001694934,0.001975262,0.0001984476,0.0002794675,0.00009961614,0.001056384,0.0004220284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004608856,"about_ca_system_score_gemma":0.00060064,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009338724,"about_ca_topic_score_gemma":0.01068544,"domain_scores_codex":[0.9953695,0.001027292,0.001433043,0.0008252389,0.0004133629,0.0009315984],"domain_scores_gemma":[0.9962747,0.0008570586,0.0006305983,0.0009478207,0.0009890782,0.000300784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007659612,0.0008216713,0.5142148,0.001962335,0.00006303059,0.00001116288,0.02198098,0.2466633,0.1310343,0.001696942,0.002900507,0.07788505],"study_design_scores_gemma":[0.00007046931,0.0004778203,0.005042494,0.0003402837,0.00002138511,7.356281e-7,0.01107803,0.8937142,0.08416393,0.000360006,0.004489599,0.0002410741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808758,0.00008695731,0.01044952,0.001599966,0.002395232,0.002502094,0.0007072667,0.0006709155,0.0007122022],"genre_scores_gemma":[0.9966661,0.00009708932,0.0004264269,0.0002833212,0.0003985734,0.0006586697,0.000210281,0.00006717596,0.001192353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6470509,"threshold_uncertainty_score":0.9999283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1330360776437908,"score_gpt":0.4441641436887612,"score_spread":0.3111280660449704,"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."}}