{"id":"W2520523926","doi":"10.1609/aaai.v30i1.9999","title":"Survival Prediction by an Integrated Learning Criterion on Intermittently Varying Healthcare Data","year":2016,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Generalizability theory; Health care; Computer science; Class (philosophy); Healthcare system; Machine learning; Data mining; Artificial intelligence; Econometrics; Statistics; Mathematics; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001390428,0.0003403451,0.0003447171,0.0001949942,0.0004221395,0.000336149,0.004315415,0.000165584,0.00006152777],"category_scores_gemma":[0.001584162,0.0002190873,0.00007309309,0.0006312937,0.0002135371,0.001289567,0.0009314172,0.000840779,0.00007461609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001483157,"about_ca_system_score_gemma":0.0001583707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003873598,"about_ca_topic_score_gemma":0.00003337741,"domain_scores_codex":[0.9964975,0.0002010771,0.0007961747,0.001155805,0.0008125578,0.0005369589],"domain_scores_gemma":[0.9971421,0.0002526367,0.0005852782,0.0009509895,0.0008558843,0.0002130476],"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":[0.000208149,0.0001975104,0.005290178,0.0001002338,0.00001742263,0.000001102217,0.001428616,0.00006016972,0.03522217,0.1608154,0.0004009813,0.796258],"study_design_scores_gemma":[0.0002393253,0.004488138,0.002751562,0.004359726,0.00003039009,0.00002807755,0.001731781,0.7613314,0.1875604,0.03185867,0.004480714,0.001139804],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4967199,0.00009439351,0.4087276,0.08086792,0.005203881,0.001964579,0.0003129094,0.001637983,0.004470848],"genre_scores_gemma":[0.9971096,0.00006870769,0.001993906,0.0003550992,0.0001338653,0.00002277977,0.00001536829,0.00002943855,0.0002712548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7951183,"threshold_uncertainty_score":0.8934119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.139214956779465,"score_gpt":0.3530516355418152,"score_spread":0.2138366787623501,"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."}}