{"id":"W3209235691","doi":"10.2196/32698","title":"A BERT-Based Generation Model to Transform Medical Texts to SQL Queries for Electronic Medical Records: Model Development and Validation","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Topic Modeling","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Baidu","keywords":"Computer science; SQL; Information retrieval; Stored procedure; Query by Example; Programming language; Natural language processing; Artificial intelligence; Database; Data mining; Search engine; Web search query","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":[0.001438742,0.0002214807,0.0003130165,0.0001474205,0.0001850023,0.0001535834,0.0006616731,0.0003674005,0.00005898256],"category_scores_gemma":[0.0008297931,0.0001987042,0.00006050507,0.0003079075,0.00004214348,0.0005102794,0.0002252028,0.0003514364,0.00001470758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002014231,"about_ca_system_score_gemma":0.005420197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003813418,"about_ca_topic_score_gemma":0.0002657794,"domain_scores_codex":[0.9957363,0.0000362701,0.001031122,0.0003082739,0.002311889,0.0005761327],"domain_scores_gemma":[0.9980773,0.0001379988,0.00008261194,0.0003701079,0.0002265976,0.001105415],"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":[0.00003728534,0.0001963301,0.00001620899,0.0003559232,0.00004228968,0.0000119351,0.02076118,0.03978771,0.00004688983,0.05862058,0.005541167,0.8745825],"study_design_scores_gemma":[0.0007574152,0.00008280922,0.000002024973,0.0001511607,0.000006121685,0.00002431548,0.00009429803,0.9869909,0.002664318,0.001660954,0.007314952,0.0002506877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0696858,0.00002718468,0.9103395,0.01887557,0.0001425556,0.0005694952,0.000003614949,0.0001162012,0.0002400688],"genre_scores_gemma":[0.188217,0.00004318279,0.7886032,0.02185632,0.0001963095,0.0007633762,0.0001027469,0.00002308104,0.0001948157],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9472032,"threshold_uncertainty_score":0.9615197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03404106744262877,"score_gpt":0.3036375694769867,"score_spread":0.2695965020343579,"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."}}