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.

Objective: The Block Design Test (BDT) is known to be an effective measure in diagnosing age-related cognitive decline of visuospatial function. The goal of this study is to investigate the effects of age, education years, and gender on the performance of the BDT and to provide normative data in Korean community-dwelling participants who are 55 to 90 years old. Methods: The participants were 432 non-demented adults aging from 55 to 90 years old. The BDT was administered to participants according to its manual. Multiple linear regressions and analyses of variance were conducted, including age, gender, and educations were used as covariates. Results: Age, educational years, and gender were found to be significantly associated with performance on the BDT. As age increased, BDT performance decreased. Educational years were associated with BDT performance. Men showed higher performance (29.9±10.3) compare to women (26.1±8.7). The BDT is influenced by age, educational years, and gender. Conclusion Unlike the previous study, the current study shows that gender has a significant influence in visuospatial ability in the old population. Present normative data will be useful for clinicians in evaluating aging participants with cognitive impairment.

Objective: The Block Design Test (BDT) is known to be an effective measure in diagnosing age-related cognitive decline of visuospatial function. The goal of this study is to investigate the effects of age, education years, and gender on the performance of the BDT and to provide normative data in Korean community-dwelling participants who are 55 to 90 years old. Methods: The participants were 432 non-demented adults aging from 55 to 90 years old. The BDT was administered to participants according to its manual. Multiple linear regressions and analyses of variance were conducted, including age, gender, and educations were used as covariates. Results: Age, educational years, and gender were found to be significantly associated with performance on the BDT. As age increased, BDT performance decreased. Educational years were associated with BDT performance. Men showed higher performance (29.9±10.3) compare to women (26.1±8.7). The BDT is influenced by age, educational years, and gender. Conclusion Unlike the previous study, the current study shows that gender has a significant influence in visuospatial ability in the old population. Present normative data will be useful for clinicians in evaluating aging participants with cognitive impairment.

Sclerotic fibroma is an uncommon benign neoplasm characterized by asymptomatic, skin-colored, or pinkish papules or nodules in young and middle-aged adults of both sexes. In this case report, dermoscopic findings showed a homogenous, structureless white lesion with perilesional erythema and peripheral arborizing vessels. Herein, we described an unusual case of a periungual sclerotic fibroma with distinct dermoscopic features.

Congenital cutaneous candidiasis (CCC) is a rare disease caused by Candida spp. that occurs within the first six days of life. Its exact pathogenesis remains unclear; however, the suspected pathomechanisms include maternal vulvovaginal candidiasis and ascending infections. A preterm, 1,550-g male infant presented with generalized maculopapules and pustules on his whole body. The patient’s mother had undergone cervical cerclage at a gestational age (GA) of 29 weeks due to an incompetent internal os of the cervix. The pregnancy was terminated at GA 37-week because the mother developed chorioamnionitis. We performed a potassium hydroxide microscopic examination, skin biopsy, and fungal culture test on the baby. Microscopic examination of the skin scrapings revealed pseudohyphae with yeasts, and Candida albicans was identified in the culture test. Maternal placental biopsy revealed fungal organisms, and the baby was diagnosed with CCC due to an ascending infection. The skin lesions completely disappeared after intravenous liposomal amphotericin B treatment.