Background: Rapid triage reduces the patients' stay time at an emergency department (ED). The Korean Triage Acuity Scale (KTAS) is mandatorily applied at EDs in South Korea.For rapid triage, we studied machine learning-based triage systems composed of a speech recognition model and natural language processing-based classification. Methods: We simulated 762 triage cases that consisted of 18 classes with six types of the main symptom (chest pain, dyspnea, fever, stroke, abdominal pain, and headache) and three levels of KTAS. In addition, we recorded conversations between emergency patients and clinicians during the simulation. We used speech recognition models to transcribe the conversation. Bidirectional Encoder Representation from Transformers (BERT), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) were used for KTAS and symptom classification. Additionally, we evaluated the Shapley Additive exPlanations (SHAP) values of features to interpret the classifiers. Results: The character error rate of the speech recognition model was reduced to 25.21% through transfer learning. With auto-transcribed scripts, support vector machine (area under the receiver operating characteristic curve [AUROC], 0.86; 95% confidence interval [CI], 0.81–0.9), KNN (AUROC, 0.89; 95% CI, 0.85–0.93), RF (AUROC, 0.86; 95% CI, 0.82–0.9) and BERT (AUROC, 0.82; 95% CI, 0.75–0.87) achieved excellent classification performance.Based on SHAP, we found “stress”, “pain score point”, “fever”, “breath”, “head” and “chest” were the important vocabularies for determining KTAS and symptoms. Conclusion We demonstrated the potential of an automatic KTAS classification system using speech recognition models, machine learning and BERT-based classifiers.

Background: Rapid triage reduces the patients' stay time at an emergency department (ED). The Korean Triage Acuity Scale (KTAS) is mandatorily applied at EDs in South Korea.For rapid triage, we studied machine learning-based triage systems composed of a speech recognition model and natural language processing-based classification. Methods: We simulated 762 triage cases that consisted of 18 classes with six types of the main symptom (chest pain, dyspnea, fever, stroke, abdominal pain, and headache) and three levels of KTAS. In addition, we recorded conversations between emergency patients and clinicians during the simulation. We used speech recognition models to transcribe the conversation. Bidirectional Encoder Representation from Transformers (BERT), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) were used for KTAS and symptom classification. Additionally, we evaluated the Shapley Additive exPlanations (SHAP) values of features to interpret the classifiers. Results: The character error rate of the speech recognition model was reduced to 25.21% through transfer learning. With auto-transcribed scripts, support vector machine (area under the receiver operating characteristic curve [AUROC], 0.86; 95% confidence interval [CI], 0.81–0.9), KNN (AUROC, 0.89; 95% CI, 0.85–0.93), RF (AUROC, 0.86; 95% CI, 0.82–0.9) and BERT (AUROC, 0.82; 95% CI, 0.75–0.87) achieved excellent classification performance.Based on SHAP, we found “stress”, “pain score point”, “fever”, “breath”, “head” and “chest” were the important vocabularies for determining KTAS and symptoms. Conclusion We demonstrated the potential of an automatic KTAS classification system using speech recognition models, machine learning and BERT-based classifiers.

We report the prenatal diagnosis of an unbalanced translocation between chromosome Y and chromosome 15 in a female fetus. Cytogenetic analysis of parental chromosomes revealed that the mother had a normal 46,XX karyotype, whereas the father exhibited a 46,XY,der(15)t(Y;15) karyotype. We performed cytogenetic analysis of the father's family as a result of the father and confirmed the same karyotype in his mother and brother. Fluorescence in situ hybridization and quantitative fluorescent-polymerase chain reaction analysis identified the breakpoint and demonstrated the absence of the SRY gene in female members. Thus, the proband inherited this translocation from the father and grandmother. This makes the prediction of the fetal phenotype possible through assessing the grandmother. Therefore, we suggest that conventional cytogenetic and molecular cytogenetic methods, in combination with family history, provide informative results for prenatal diagnosis and prenatal genetic counseling.
Chromosomes, Human, Pair 15* ; Cytogenetic Analysis ; Cytogenetics ; Fathers ; Female* ; Fetus* ; Fluorescence ; Genes, sry ; Genetic Counseling ; Grandparents ; Humans ; In Situ Hybridization ; Karyotype ; Mothers ; Parents ; Phenotype ; Prenatal Diagnosis* ; Sex Chromosome Aberrations ; Siblings

PURPOSE: The 2017 international consensus guidelines (ICG) for intraductal papillary mucinous neoplasm (IPMN) of the pancreas were recently released. Important changes included the addition of worrisome features such as elevated serum CA 19-9 and rapid cyst growth (>5 mm over 2 years). We aimed to clinically validate the 2017 ICG and compare the diagnostic performance between the 2017 and 2012 ICG. METHODS: This was a retrospective cohort study. During January 2000–January 2017, patients who underwent complete surgical resection and had pathologic confirmation of branch-duct or mixed-type IPMN were included. To evaluate diagnostic performance, the areas under the receiver operating curves (AUCs) were evaluated. RESULTS: A total of 448 patients were included. The presence of mural nodule (hazard ratio [HR], 9.12; 95% confidence interval [CI], 4.60–18.09; P = 0.001), main pancreatic duct dilatation (>5 mm) (HR, 5.32; 95% CI, 2.67–10.60; P = 0.001), thickened cystic wall (HR, 3.40; 95% CI, 1.51–7.63; P = 0.003), and elevated CA 19-9 level (>37 unit/mL) (HR, 5.25; 95% CI, 2.05–13.42; P = 0.001) were significantly associated with malignant IPMN. Malignant lesions showed a cyst growth rate >5 mm over 2 years more frequently than benign lesions (60.9% vs. 29.7%, P = 0.012). The AUC was higher for the 2017 ICG than the 2012 ICG (0.784 vs. 0.746). CONCLUSION: The new 2017 ICG for IPMN is clinically valid, with a superior diagnostic performance to the 2012 ICG. The inclusion of elevated serum CA 19-9 level and cyst growth rate to the 2017 ICG is appropriate.
Area Under Curve ; Carcinoma, Pancreatic Ductal ; Cohort Studies ; Consensus ; Dilatation ; Humans ; Mucins ; Pancreas ; Pancreatic Ducts ; Retrospective Studies