• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.4
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• pp.28-34
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• 2010

In this paper, we proposes a emergency medical information system for predicting emergency situation by using the body's vital signs. Main research of existing emergency system has focused on body sensor networks. The problem of these studies have a delay of the emergency first aid since occurring of an emergency situation send a message of emergency situation to user. In the serious situation, patients of these problem can lead to death. To solve this problem, it need to the prediction of emergency situation for doing quickly the First Aid with identify signs of a pre-emergency situations until an emergency occurs. In this paper, the sensor network technology, the security technology, the internet information retrieval techniques, data mining technology, and medical information are studied for the convergence of medical information systems of the prediction of emergency situations.

• Journal of The Korean Society of Emergency Medicine
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• v.29 no.5
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• pp.455-464
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• 2018

Objective: Machine learning is not yet widely used in the medical field. Therefore, this study was conducted to compare the performance of preexisting severity prediction models and machine learning based models (random forest [RF], gradient boosting [GB]) for mortality prediction in pneumonia patients. Methods: We retrospectively collected data from patients who visited the emergency department of a tertiary training hospital in Seoul, Korea from January to March of 2015. The Pneumonia Severity Index (PSI) and Sequential Organ Failure Assessment (SOFA) scores were calculated for both groups and the area under the curve (AUC) for mortality prediction was computed. For the RF and GB models, data were divided into a test set and a validation set by the random split method. The training set was learned in RF and GB models and the AUC was obtained from the validation set. The mean AUC was compared with the other two AUCs. Results: Of the 536 investigated patients, 395 were enrolled and 41 of them died. The AUC values of PSI and SOFA scores were 0.799 (0.737-0.862) and 0.865 (0.811-0.918), respectively. The mean AUC values obtained by the RF and GB models were 0.928 (0.899-0.957) and 0.919 (0.886-0.952), respectively. There were significant differences between preexisting severity prediction models and machine learning based models (P<0.001). Conclusion: Classification through machine learning may help predict the mortality of pneumonia patients visiting the emergency department.

• The Korean Journal of Emergency Medical Services
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• v.26 no.2
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• pp.7-18
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• 2022

Purpose: This study aimed to develop a pre-hospital subarachnoid hemorrhage (SAH) prediction tool by analyzing the extant predictive factors of patients with non-traumatic SAH who visited the hospital through the 119 emergency medical services. Methods: We retrospectively reviewed pre-hospital care reports (PCRs) and electronic medical records (EMRs) of 103 patients with non-traumatic SAH who were transported to the emergency department of two national hospitals via the 119 emergency medical service from January 1, 2017 to December 31, 2020. Variables required to apply the Ottawa SAH Rule and EMERALD SAH Rule, which are early prediction tools for SAH, were extracted and applied. Results: The most common symptoms-which were found in 94.1% and 97.0% of all patients according to PCRs and EMRs, respectively-appeared in the following order: headache, altered state of consciousness, and nausea/vomiting. When the variables used for the EMERALD Rule, namely systolic blood pressure (SBP), diastolic blood pressure (DBP), and blood sugar test (BST), were applied, the sensitivities of EMR and PCRs were 99.9% and 92.2%, respectively. Conclusion: For the timely prediction of SAH at the pre-hospital phase, patient age and symptoms should be assessed, and SBP, DBP, and BST should be measured to transport the patient to an appropriate hospital.

• Journal of the Korean Society of Safety
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• v.16 no.3
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• pp.117-124
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• 2001

This paper introduces m analysis framework and procedure for the support of the cognitive error analysis of emergency tasks in nuclear poler plants. The framework provides a new perspective in the utilization of influencing factors into error prediction. The framework can be characterized by two features. First, influencing factors that affect the occurrence of human error me classified into three groups, i.e., task characteristic factors(TCF), situation factors(SF), and performance assisting factors(PAF). This classification aims to support error prediction from the viewpoint of assessing the adequacy of PAF under given TCF and SF. Second, the assessment of influencing factors is made by each cognitive function. Through this, influencing factors assessment and error prediction can be made in an integrative way according to each cognitive function. In addition, it helps analysts identify vulnerable cognitive functions and error factors, and obtain specific nor reduction strategies. The proposed framework was applied to the error analysis of the bleed and feed operation of nuclear emergency tasks.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.7
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• pp.275-280
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• 2018

As the global population becomes aging, the demand for health services for the elderly is expected to increase. In particular, The elderly visiting the emergency department sometimes have complex medical, social, and physical problems, such as having a variety of illnesses or complaints of unusual symptoms. The proposed system is designed to predict the mortality of the elderly patients who are over 65 years old and have admitted the emergency department. For mortality prediction, we compare the support vector machines and Feed Forward Neural Network (FFNN) trained with medical data such as age, sex, blood pressure, body temperature, etc. The results of the FFNN with a hidden layer are best in the mortality prediction, and F1 score and the AUC is 52.0%, 88.6% respectively. If we improve the performance of the proposed system by extracting better medical features, we will be able to provide better medical services through an effective and quick allocation of medical resources for the elderly patients visiting the emergency department.

• Journal of Trauma and Injury
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• v.25 no.4
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• pp.139-144
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• 2012

Purpose: Hypovolemic shock is the leading cause of death in multiple trauma patients with pelvic bone fracures. The purpose of this study was to develop a simple prediction method for injury severity and amount of hemorrhage via an analysis of the correlation between the site of pelvic bone fracture and the amount of transfusion and to verify the usefulness of the such a simple scoring system. Methods: We analyzed retrospectively the medical records and radiologic examination of 102 patients who had been diagnosed as having a pelvic bone fracture and who had visited the Emergency Department between January 2007 and December 2011. Fracture sites in the pelvis were confirmed and re-classified anatomically as pubis, ilium or sacrum. A multiple linear regression analysis was performed on the amount of transfusion, and a simplified scoring system was developed. The predictive value of the amount of transfusion for the scoring system as verified by using the receiver operating characteristics (ROC). The area under the curve of the ROC was compared with the injury severity score (ISS). Results: From among the 102 patients, 97 patients (M:F=68:29, mean $age=46.7{\pm}16.6years$) were enrolled for analysis. The average ISS of the patients was $16.2{\pm}7.9$, and the average amount of packed RBC transfusion for 24 hr was $3.9{\pm}4.6units$. The regression equation resulting from the multiple linear regression analysis was 'packed RBC units=1.40${\times}$(sacrum fracture)+1.72${\times}$(pubis fracture)+1.67${\times}$(ilium fracture)+0.36' and was found to be suitable (p=0.005). We simplified the regression equation to 'Pelvic Bleeding Score=sacrum+pubis+ilium.' Each fractured site was scored as 0(no fracture) point, 1(right or left) point, or 2(both) points. Sacrum had only 0 or 1 point. The score ranged from 0 to 5. The area under the curve (AUC) of the ROC was 0.718 (95% CI: 0.588-0.848, p=0.009). For an upper Pelvis Bleeding Score of 3 points, the sensitivity of the prediction for a massive transfusion was 71.4%, and the specificity was 69.9%. Conclusion: We developed a simplified scoring system for the anatomical fracture sites in the pelvis to predict the requirement for a transfusion (Pelvis Bleeding Score (PBS)). The PBS, compared with the ISS, is considered a useful predictor of the need for a transfusion during initial management.

• Journal of Preventive Medicine and Public Health
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• v.51 no.3
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• pp.154-162
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• 2018

Objectives: Early retirement occurs when one's job satisfaction suffers due to employment mismatch resulting from factors such as inadequate compensation. Medical doctors report high levels of job stress and burnout relative to other professionals. These levels are highest among emergency physicians (EPs), and despite general improvements in their working conditions, early retirement continues to become more common in this population. The purpose of this study was to identify the factors influencing EPs intention to retire early and to develop a probability equation for its prediction. Methods: A secondary analysis of data from the 2015 Korean Society of Emergency Physicians Survey was performed. The variables potentially influencing early retirement were organized into personal characteristics, extrinsic factors, and intrinsic factors. Logistic regression analysis was performed to identify risk factors and to develop a probability equation; these findings were then arranged in a nomogram. Results: Of the 377 survey respondents included in the analysis, 48.0% intended to retire early. Risk factors for early retirement included level of satisfaction with the specialty and its outlook, slanderous reviews, emergency room safety, health status, workload intensity, age, and hospital type. Intrinsic factors (i.e., slanderous reviews and satisfaction with the specialty and its outlook) had a stronger influence on early retirement than did extrinsic factors. Conclusions: To promote career longevity among EPs, it is vital to improve emergency room safety and workload intensity, to enhance medical professionalism through a stronger vision of emergency medicine, and to strengthen the patient-doctor relationship.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.11-23
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• 2024

Triage is a practice of accurately prioritizing patients in emergency department (ED) based on their medical condition to provide them with proper treatment service. The variation in triage assessment among medical staff can cause mis-triage which affect the patients negatively. Developing ED triage system based on machine learning (ML) techniques can lead to accurate and efficient triage outcomes. This study aspires to develop a triage system using machine learning techniques to predict ED triage levels using patients' information. We conducted a retrospective study using Security Forces Hospital ED data, from 2021 through 2023 during Hajj period in Saudia Arabi. Using demographics, vital signs, and chief complaints as predictors, two machine learning models were investigated, naming gradient boosted decision tree (XGB) and deep neural network (DNN). The models were trained to predict ED triage levels and their predictive performance was evaluated using area under the receiver operating characteristic curve (AUC) and confusion matrix. A total of 11,584 ED visits were collected and used in this study. XGB and DNN models exhibit high abilities in the predicting performance with AUC-ROC scores 0.85 and 0.82, respectively. Compared to the traditional approach, our proposed system demonstrated better performance and can be implemented in real-world clinical settings. Utilizing ML applications can power the triage decision-making, clinical care, and resource utilization.

• Journal of The Korean Society of Emergency Medicine
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• v.29 no.5
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• pp.430-436
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• 2018

Objective: The purpose of this study was to evaluate whether or not the d-dimer level indicating hyperfibrinolysis could be a predictor of early poor outcome (massive transfusion, death within 24 hours) associated with trauma-induced coagulopathy in blunt trauma without significant brain injury. Methods: This study was a retrospective observational study using 516 blunt trauma patients without significant brain injury. The poor outcome group, including patients receiving massive transfusion and those who died within 24 hours, consisted of 33 patients (6.4%). The variables were compared between the poor outcome group and good outcome group, and logistic regression analysis was performed using statistically significant variables. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the poor outcome prediction ability of the initial d-dimer level. Results: The poor outcome group showed more serious anatomical, physiological, and laboratory data than the good outcome group. In the ROC curve analysis for evaluation of the poor outcome prediction of the d-dimer level, the area under the curve value was 0.87 (95% confidence interval [CI], 0.84-0.90) while the cut-off value was 27.35 mg/L. In the logistic regression analysis, the high d-dimer level was shown to be an independent predictor of poor outcome (adjusted odds ratio, 14.87; 95% CI, 2.96-74.67). Conclusion: The high d-dimer level (>27.35 mg/L) can be used as a predictor for the poor outcome of patients with blunt trauma without significant brain injury.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.747-750
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• 1995

As railway trains run faster high performance braking system are necessary because more energy needs to be dissipated due to increased kinetic energy. In this work a portable computer based prediction system for emergency braking distance has been developed. The algorithm for the system is based on braking theory and empirical results of actual braking test. The computer is connected to the sensors to measure the velocity and the braking pressure in real train. It is expected that this system will be utilized to predict emergency braking distance during actual operation of the train