{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005900635,0.001499971,0.0006614396,0.001136394,0.0003776622,0.0007230059,0.001324493,0.0008736544,0.0009112934],"category_scores_gemma":[0.00917357,0.0004981727,0.0009667865,0.0008317113,0.0004445865,0.0009682798,0.001166022,0.001843131,0.0003182756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001908898,"about_ca_system_score_gemma":0.001957978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01841848,"about_ca_topic_score_gemma":0.01238568,"domain_scores_codex":[0.9991278,0.0003790469,0.00008916322,0.0001898827,0.0001288428,0.00008524933],"domain_scores_gemma":[0.9952589,0.003111166,0.0003267457,0.0002654489,0.0009380246,0.00009982454],"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":[0.0004093472,0.0006335007,0.04287213,0.000172923,0.0002817811,0.0001008379,0.00007045038,0.8684559,0.0009972864,0.000691828,0.001509691,0.08380431],"study_design_scores_gemma":[0.00001275962,0.00008389497,0.001594651,0.00001414305,0.00001933851,0.000009200033,0.000009437392,0.997136,0.0005457365,0.0004713702,0.00009742722,0.000006046062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8598039,0.002008238,0.1315835,0.0008817944,0.0001513886,0.0005221704,0.002371875,0.00073319,0.001943929],"genre_scores_gemma":[0.9447296,0.000506913,0.05073026,0.0001455131,0.00003475813,0.0005892675,0.002506482,0.00002679282,0.0007305028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01841848,"threshold_uncertainty_score":0.03662258,"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."}}