{"id":"W4390136468","doi":"10.48550/arxiv.2312.13454","title":"MixEHR-SurG: a joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health records","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Health records; Hazard; Joint (building); Health hazard; Electronic health record; Computer science; Data science; Data mining; Medicine; Environmental health; Engineering; Political science; Health care; Civil engineering; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.006886895,0.001322498,0.001370648,0.001816956,0.0005716482,0.001105081,0.002094827,0.001363235,0.002286826],"category_scores_gemma":[0.01033906,0.0006511947,0.002615695,0.001249643,0.0006344229,0.001683813,0.001603449,0.002309415,0.001270042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008424452,"about_ca_system_score_gemma":0.001340731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008436671,"about_ca_topic_score_gemma":0.01238884,"domain_scores_codex":[0.9978807,0.001167749,0.0001127581,0.0005614178,0.0001715706,0.0001058662],"domain_scores_gemma":[0.9918108,0.006944464,0.0003840166,0.00035977,0.0003605666,0.0001402845],"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":[0.001621096,0.0007389018,0.06169263,0.0007169456,0.001272616,0.0005816452,0.001758241,0.443493,0.004743378,0.0259536,0.02286922,0.4345588],"study_design_scores_gemma":[0.00009148294,0.00008189396,0.002656247,0.0000315732,0.0001012274,0.00008930248,0.00006851958,0.9796407,0.0005196236,0.01441626,0.002269531,0.00003360668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08396684,0.002051845,0.9034182,0.001446945,0.0001829244,0.0004083888,0.004576121,0.002819388,0.001129352],"genre_scores_gemma":[0.6803985,0.001669991,0.2908432,0.0010073,0.001025833,0.001225964,0.01611391,0.0004078083,0.007307577],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008436671,"threshold_uncertainty_score":0.03642184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2372352507708456,"score_gpt":0.255209623572862,"score_spread":0.01797437280201644,"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."}}