• Proceedings of the Korea Contents Association Conference
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• 2012.05a
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• pp.343-344
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• 2012

본 연구는 중증도가 높은 신경계중환자를 대상으로 중환자 중증도 분류도구와 Glasgow coma scale 적용의 유용성을 검정하고자 하는데 있다. 본 연구에서 대상자의 일반적 특성 및 임상 관련 특성에 따른 사망률 확인, 중환자 중증도 분류도구(CPSCS)의 일반적 특성, 임상관련 특성에 따른 중증도 차이, GCS의 일반적 특성과 임상관련 특성에 따른 중증도 차이를 파악하고, 임상적 유용성을 검정하고자 한다.

• Journal of the Society of Disaster Information
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• v.14 no.1
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• pp.28-35
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• 2018

The following research has focused and implemented on designing a system that classifies the severity of mass casualty situations across both normal and disaster levels. The system's algorithm has implemented requirements such as accuracy as well as user convenience. The developed e-Triage System has applied various severity classification algorithms implemented from IoT concepts. In order to overcome flaws of currently used severity classification systems, the e-Triage System used electronic elements including the NFC module. By using the mobile application's severity classification algorithm the system demonstrated quick and accurate assessment of patient. Four different LED lamps visualized the severity classification results and RTS scores were portrayed through FND(Flexible Numeric Display) after a two wave classification.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.317-320
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• 2021

본 연구는 주기적인 알츠하이머 병의 중증도 모니터링을 위해 스마트 인솔을 통한 보행 특징 추출과 머신러닝 기반 중증도 분류의 성능에 대해 살펴보았다. 최근 고령화가 가속화되는 추세에 있어 치매 환자가 급증하고 있으며, 중증도가 심해질수록 필요한 치료 비용 및 노력이 급증하기 때문에 조기 진단이 최선의 치료 전략으로 보여진다. 환자 친화적이고 저비용의 관성 측정 장치가 내장된 스마트 인솔만을 사용하여 다양한 보행 실험 패러다임에서 환자의 보행 특징을 추출하고, 이를 알츠하이머 병의 중증도 진단을 위한 머신러닝 기반 분류기를 훈련시켜 성능을 평가한 결과, 숫자세기와 같이 뇌에 부하를 주는 하위 작업이 포함된 복합 보행을 측정한 데이터셋을 사용하여 훈련된 분류 모델이 일반 걷기 데이터셋을 사용한 모델보다 성능이 높게 나타나는 것이 관찰되었다. 본 연구는 안전하고 환경적 제약이 적은 방법을 사용하여 시기 적절한 진단뿐만 아니라 주기적인 중증도 모니터링 시스템의 일환으로 활용될 수 있을 것이다.

• Tuberculosis and Respiratory Diseases
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• v.49 no.5
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• pp.585-593
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• 2000

Background : International consensus guidelines have recently been developed to improve the assessment and management of asthma. One of the major recommendation of these guidelines is that asthma severity should be assessed through the recognition of key symptoms, such as nocturnal waking, medication requirements, and objective measurements of lung function. Differential classification of asthma severity would lead to major differences in both long term pharmacological management and the treatment of severe exacerbation. Methods : This study examined the relationship between the symptom score and measurements of $FEV_1$ and PEF when expressed as a percentage of predicted values in asthmatics (n=107). Results : The correlation of $FEV_1$ % with PEFR% was highly significant (r=0.83, p<0.01). However, there was agreement in terms of the classification of asthma severity in 76.6% of the paired measurements of $FEV_1$ % and PEFR%. Agreement in the classification of asthma severity was also found in 57.1% of the paired analysis of $FEV_1$ % and symptom score. 39% of the patients classified as having moderate asthma on the basis of $FEV_1$ % recording would be considered to have severe asthma if symptom score alone were used. Low baseline $FEV_1$ and high bronchial responsiveness were associated with a low degree of perception of airway obstruction. Conclusion : The relationships between the symptom score, PEFR and $FEV_1$ were generally poor. When assessing asthma severity, age, duration, $PC_{20}$, and baseline $FEV_1$ should be considered.

• Journal of Digital Convergence
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• v.11 no.5
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• pp.371-384
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• 2013

The purpose of this study was to develop the risk-adjusted mortality model using Korean Hospital Discharge Injury data and US National Hospital Discharge Survey data and to suggest some ways to manage hospital mortality rates through comparison of Korea and United States Hospital Standardized Mortality Ratios(HSMR). This study used data mining techniques, decision tree and logistic regression, for developing Korea and United States risk-adjustment model of in-hospital mortality. By comparing Hospital Standardized Mortality Ratio(HSMR) with standardized variables, analysis shows the concrete differences between the two countries. While Korean Hospital Standardized Mortality Ratio(HSMR) is increasing every year(101.0 in 2006, 101.3 in 2007, 103.3 in 2008), HSMR appeared to be reduced in the United States(102.3 in 2006, 100.7 in 2007, 95.9 in 2008). Korean Hospital Standardized Mortality Ratios(HSMR) by hospital beds were higher than that of the United States. A two-aspect approach to management of hospital mortality rates is suggested; national and hospital levels. The government is to release Hospital Standardized Mortality Ratio(HSMR) of large hospitals and to offer consulting on effective hospital mortality management to small and medium hospitals.

• Fire Science and Engineering
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• v.33 no.2
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• pp.139-144
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• 2019

The increase in the elderly population also increased the damage and deaths of the elderly drivers. However, studies on the severity and severity of the elderly driver are not actively conducted and the factors are unknown. In this study, I tried to find out the factors affecting the damage and severity of the elderly driver in the frontal collision and to utilize them additionally in the severity classification. Collision Deformation Classification (CDC) Code was used to check the extent of damage to the vehicle. Abbreviated Injury Scale (AIS) was used to determine the injury parts and severity of injury, and the Injury Severity Score (ISS) to confirm the severity of the patient. The odds ratios of severe injury patients were found to be 7.381 in the subjects with 5 or more deformation extent and the ${\beta}$ value of the deformation extent was 0.453 in the analysis of the severity by linear regression analysis. Therefore, the degree of deformation extent of 5 or more can be suggested as a criterion that can be used additionally to the severity classification in the elderly driver.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2672-2679
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• 2012

The study was done to provide basic data of medical quality evaluation after developing the comorbidity disease mortality measurement modeled on the severity-adjustment method of AMI. This study analyzed 699,701 cases of Hospital Discharge Injury Data of 2005 and 2008, provided by the Korea Centers for Disease Control and Prevention. We used logistic regression to compare the risk-adjustment model of the Charlson Comorbidity Index with the predictability and compatibility of our severity score model that is newly developed for calibration. The models severity method included age, sex, hospitalization path, PCI presence, CABG, and 12 variables of the comorbidity disease. Predictability of the newly developed severity models, which has statistical C level of 0.796(95%CI=0.771-0.821) is higher than Charlson Comorbidity Index. This proves that there are differences of mortality, prevalence rate by method of mortality model calibration. In the future, this study outcome should be utilized more to achieve an improvement of medical quality evaluation, and also models will be developed that are considered for clinical significance and statistical compatibility.

• Journal of Convergence for Information Technology
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• v.10 no.4
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• pp.98-103
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• 2020

The purpose of this study was to determine whether the results of KTAS(Korean Triage and Acuity Scale) triage classifier differ according to the occupations. We analyzed a total of 10,960,359 cases of data sent to the NEDIS from January 1st, 2016 to December 31th, 2017. The triage classifier were MD(Medical Doctor), R(Resident), INT(Intern), GP(General Practitioner), RN(Registered Nurses) and EMT(Emergency Medical Technician). The consistency between the initial triage and final triage results was the highest GP(98.9%) and the lowest INT(80.2%). The results of over-triage classification was the lowest by GP(0.6%) and the highest for INT(16.0%). Also, the results of under-triage classification was the lowest by MD, EMT(0.4%) and the highest for INT(3.8%). The results of KTAS triage classifier significantly differ from according to the occupations(p<0.001). Triage classification should not differ from according to occupations and skill. It is necessary to strengthen the classifier's capacity for accurate triage classifications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.626-634
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• 2018

This retrospective study investigates the accuracy of triage procedures for pediatric patients in emergency departments (EDs) using the Korean Triage and Acuity Scale (KTAS). The study includes 250 randomly selected initial nursing records and clinical outcomes of pediatric patients who visited one regional ED or a local ED from October 2016 to September 2017. The collected data were analyzed by a qualified expert to determine the true triage score. The accuracy of triage was defined as the agreement between the triage score of the emergency nurses (ENs) and the true triage score as determined by the expert. Based on expert comments, the cause of the triage error was analyzed and the KTAS score was compared with the discharge, length of stay (LOS), and medical cost. The results showed that the degree of agreement in the triage score between the experts and the ENs was excellent (weighted kappa=0.77). Among the causes of triage discordance, the most frequent was the incorrect application of vital signs to the KTAS algorithm criteria (n=13). Patients with high severity KTAS levels 1 and 2 were discharged less often (${\chi}=43.25$, p<0.001). There were significant differences in the length of stay (F=12.39, p<0.001) and cost (F=11.78, p<0.001) between KTAS scores when adjusting for age. The results of this study indicate that KTAS is highly accurate in EDs. Hence, the newly developed triage tool is becoming well established in Korea.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.217-230
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• 2019

This study was conducted to develop a customized severity-adjustment method and to evaluate their validity for acute myocardial infarction(AMI) patients to complement the limitations of the existing severity-adjustment method for comorbidities. For this purpose, the subjects of KCD-7 code I20.0 ~ I20.9, which is the main diagnosis of acute myocardial infarction were extracted using the Korean National Hospital Discharge In-depth Injury survey data from 2006 to 2015. Three tools were used for severity-adjustment method of comorbidities : CCI (charlson comorbidity index), ECI (Elixhauser comorbidity index) and the newly proposed CCS (Clinical Classification Software). The results showed that CCS was the best tool for the severity correction, and that support vector machine model was the most predictable. Therefore, we propose the use of the customized method of severity correction and machine learning techniques from this study for the future research on severity adjustment such as assessment of results of medical service.