Automatic Classification of the Korean Triage Acuity Scale in Simulated Emergency Rooms Using Speech Recognition and Natural Language Processing: a Proof of Concept Study

• DOI: 10.3346/jkms.2021.36.e175
• Publication Date: 2021-06-14T00:48:35Z
• Authors: Dongkyun Kim; Jaehoon Oh; Heeju Im; Myeongseong Yoon; Jiwoo Park; Joohyun Lee
• Keywords: Adult; 02 engineering and technology; Middle Aged; Proof of Concept Study; Patient Simulation; Deep Learning; Republic of Korea; Emergency Medicine; Speech Perception; 0202 electrical engineering, electronic engineering, information engineering; Humans; Original Article; Triage; Emergency Service, Hospital; Aged; Natural Language Processing; Retrospective Studies

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.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.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.We demonstrated the potential of an automatic KTAS classification system using speech recognition models, machine learning and BERT-based classifiers.