{"id":"W4229455170","doi":"10.2196/38241","title":"Predicting Postoperative Mortality With Deep Neural Networks and Natural Language Processing: Model Development and Validation","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Far Eastern Memorial Hospital; Ministry of Science and Technology, Taiwan","keywords":"Receiver operating characteristic; Unstructured data; Medicine; Artificial neural network; Artificial intelligence; Natural language processing; Deep learning; Text mining; Recall; Computer science; Machine learning; Data mining; Big data; Psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.0005082686,0.0001164337,0.0001319115,0.00005076961,0.0004585266,0.0001301242,0.000269138,0.0000461256,0.000005087096],"category_scores_gemma":[0.00005591391,0.0000891024,0.000008152625,0.0001924509,0.00006004796,0.0005258796,0.0005141592,0.0006904402,2.101862e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005313524,"about_ca_system_score_gemma":0.0001682388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001427747,"about_ca_topic_score_gemma":0.00001966301,"domain_scores_codex":[0.9985194,0.00007470455,0.000337972,0.000133354,0.0007163501,0.0002182149],"domain_scores_gemma":[0.9994213,0.00005724964,0.0001538062,0.0001406891,0.00005561104,0.0001713552],"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":[0.00002553934,0.00005250443,0.06672961,0.000430907,0.00002786041,0.0000382999,0.2599834,0.1204437,0.000001038342,0.001533145,0.00003558709,0.5506985],"study_design_scores_gemma":[0.0002481413,0.00007088915,0.005092164,0.00002375358,0.000002727055,0.0001084445,0.002593945,0.9916948,0.000003552685,0.00001110361,0.00003864055,0.0001118534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8121106,0.0001904165,0.1866895,0.0005318499,0.00005160892,0.0002260849,7.162298e-7,0.0001102045,0.00008898436],"genre_scores_gemma":[0.9703248,0.000001662278,0.02820314,0.001337636,0.00002092527,0.00007009081,0.00002315852,0.000005771095,0.00001279001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8712511,"threshold_uncertainty_score":0.363349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01491053405729858,"score_gpt":0.3032265227222249,"score_spread":0.2883159886649263,"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."}}