Ji Hoon Kim

This study aimed to develop a machine learning-based clinical decision support system for emergency departments based on the decision-making framework of physicians. We extracted 27 fixed and 93 observation features using data on vital signs, mental status, laboratory results, and electrocardiograms during emergency department stay. Outcomes included intubation, admission to the intensive care unit, inotrope or vasopressor administration, and in-hospital cardiac arrest. eXtreme gradient boosting

BACKGROUND: Interpretation of chest radiographs (CRs) by emergency department (ED) physicians is inferior to that by radiologists. Recent studies have investigated the effect of deep learning-based assistive technology on CR interpretation (DLCR), although its relevance to ED physicians remains unclear. This study aimed to investigate whether DLCR supports CR interpretation and the clinical decision-making of ED physicians. METHODS: We conducted a prospective interventional study using a web-bas

Intermittent manual measurement of vital signs may not rapidly predict sepsis development in febrile patients admitted to the emergency department (ED). We aimed to evaluate the predictive performance of a wireless monitoring device that continuously measures heart rate (HR) and respiratory rate (RR) and a machine learning analysis in febrile but stable patients in the ED. We analysed 468 patients (age, ≥18 years; training set, n = 277; validation set, n = 93; test set, n = 98) having fever (tem

BACKGROUND: Natural language processing has been established as an important tool when using unstructured text data; however, most studies in the medical field have been limited to a retrospective analysis of text entered manually by humans. Little research has focused on applying natural language processing to the conversion of raw voice data generated in the clinical field into text using speech-to-text algorithms. OBJECTIVE: In this study, we investigated the promptness and reliability of a r

본 연구의 목적은 야외 운동기구를 이용하는 노인들의 신체적 자기개념과 이용만족, 지속적 이용의도의 관계를 종합적으로 규명하는데 있다. 연구대상은 서울시 및 경기도 일대의 근린공원 및 놀이터에 설치되어 있는야외 운동기구를 이용하는 65세 이상 노인을 모집단으로 선정하였고, 조사대상의 표집은 확률표집방법 중 집락표집(cluster sampling)을 사용하였다. 설문지는 자기평가 기입법으로 설문내용에 대하여 응답하도록 하고직접 응답이 불가능한 노인에게는 직접 면접을 실시하여 설문조사를 실시하였으며, 설문자료를 Window용SPSS 15.0 Version 통계프로그램과 AMOS 7.0을 이용하여 본 연구의 목적에 맞게 분석하였다. 이 같은 연구 목적을 바탕으로 다음과 같은 결론을 얻었다. 첫째, 신체적 자기개념 중 신체적 유능감과 건강이 이용만족에 유의한 영향을 미치는 요인으로 나타났다. 둘째, 신체적 자기개념 중 신체적 유능감과 건강이 지속적 이용에 유의한 영향을 미치는 요인으로 나타났

Critical cooling rate (Rc) for glass formation has been calculated from an integrated transformation curve, constructed by combining continuous cooling transformation (CCT) and continuous heating transformation (CHT) curves. The CCT and CHT curves were calculated from experimental measurements on cooling rate dependence of solidification onset temperature using classical nucleation kinetics and heating rate dependence of crystallization onset temperature using Kissinger method, respectively. The

In this retrospective observational study, we aimed to develop a machine-learning model using data obtained at the prehospital stage to predict in-hospital cardiac arrest in the emergency department (ED) of patients transferred via emergency medical services. The dataset was constructed by attaching the prehospital information from the National Fire Agency and hospital factors to data from the National Emergency Department Information System. Machine-learning models were developed using patient

Importance: Emergency department (ED) discharge documentation is time-consuming and often incomplete. Objective: To develop a large language model (LLM) assistant that generates ED discharge notes and to evaluate its effectiveness on documentation quality and workflow efficiency. Design, Setting, and Participants: This comparative effectiveness study, which was conducted at a 2400-bed tertiary care hospital in South Korea, consisted of 2 primary phases: a development phase and sequential validat

BACKGROUND AND PURPOSE: The early prediction of acute kidney injury (AKI) in sepsis and provision of timely treatment may improve outcomes. We investigated the efficacy of the delta neutrophil index (DNI)-which reflects the fraction of immature granulocytes-in predicting sepsis-induced AKI and 30-day mortality in cases of severe sepsis or septic shock. METHODS: This retrospective, observational cohort study was performed with patients prospectively integrated in a critical pathway of early-goal-

Understanding and predicting a driver’s behaviors in a vehicle is a prospective function embedded in a smart car. Beyond the patterns of observable behaviors, driver’s intention could be identified based on goal-driven behaviors. A computational model to classify driver intention in visual search which is finding a target with one’s eyes as moving selective attention across a search field, could improve the level of intelligence that a smart car could demonstrate. To develop a computational cogn