• Proceedings of the Korea Contents Association Conference
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• 2013.05a
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• pp.209-210
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• 2013

본 연구에서는 보건복지부에서 중증 응급환자를 위한 '중증질환별 특성화 센터'로 지정된 안양의 H병원에서 오토바이 사고로 인해 응급실을 내원하여 중증외상 환자로 분류된 환자를 대상으로 보건복지부 중앙응급의료센터에서 정한 중증외상 등록체계를 바탕으로 중증도를 분석하여 손상기전과 생존의 영향을 미치는 인자에 대하여 알아보고자 한다.

• 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.

• 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.

• Journal of Convergence for Information Technology
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• v.10 no.11
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• pp.98-106
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• 2020

This study was conducted to identify the factors influencing the severity of pressure injuries of patients with pressure injuries admitted to acute university hospital. This study was a secondary analysis on the data of the study conducted to identify the factors influencing the deterioration of pressure injuries during hospitalization. The data were collected by retrospectively examining the medical records of patients with pressure injuries who were 18 years of age or older and who were admitted to acute university hospital from May 2017 to November 2018. We used data from 472 patients with pressure injuries at admission for this secondary analysis. In order to identify the factor influencing of severe pressure injuries compare to superficial pressure injuries, we analyzed the data using logistic regression analysis. As a result of the study, gender, body temperature, and patient's movement were identified as factors affecting severe pressure injuries. Therefore, special care is necessary to increased the number of position change for inpatients with pressure injuries, especially for patients with decreased mobility.

• The Journal of the Korea Contents Association
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• v.11 no.9
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• pp.372-383
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• 2011

Our study was carried out to develop the severity-adjustment model for length of stay in hospital for percutaneous coronary interventions so that we would analysis the factors on the variation in length of stay(LOS). The subjects were 1,011 percutaneous coronary interventions inpatients of the Korean National Hospital Discharge In-depth Injury Survey 2004-2006 data. The data were analyzed using t-test and ANOVA and the severity-adjustment model was developed using data mining technique. After yielding the standardized value of the difference between crude and expected length of stay, we analysed the variation of length of stay for percutaneous coronary interventions. There was variation of LOS in regional differences, size of sickbed and insurance type. The variation of length of stay controlling the case mix or severity of illness can be explained the factors of provider. This supply factors in LOS variations should be more studied for individual practice style or patient management practices and healthcare resources or environment. We expect that the severity-adjustment model using administrative databases should be more adapted in other diseases in practical.

• Journal of Digital Convergence
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• v.11 no.6
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• pp.221-233
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• 2013

This study aims to develop the severity-adjusted length of stay(LOS) model for acute stroke patients using data from the hospital discharge survey and propose management of length of stay(LOS) for acute stroke patients and using for Hospital management. The dataset was taken from 23,134 database of the hospital discharge survey from 2004 to 2009. The severity-adjusted LOS model for the acute stroke patients was developed by data mining analysis. From decision making tree model, the main reasons for LOS of acute stroke patients were acute stroke type. The difference between severity-adjusted LOS from the decision making tree model and real LOS was compared and it was confirmed that insurance type and bed number of hospital, location of hospital were statistically associated with LOS. And to conclude, hospitals should manage the LOS of acute stroke patients applying it into the medical information system.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.175-182
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• 2011

This study proposes CAOPI(Computer Aided Organ Prediction Index) system based on APACHE II(Acute Physiology And Chronic Health Evaluation) for classifying disease severity and predicting the conditions of patients' major organs. The existing ICU disease severity evaluation is mostly about calculating risk scores using patients' data at certain points, which has limitations on making precise treatments. CAOPI system is designed to provide personalized treatments by classifying accurate severity degrees of emergency patients, predicting patients' mortality rate and scoring the conditions of certain organs.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.200-208
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• 2017

The purpose of this study was to examine the relationship of the severity, depression and to identify factors influencing quality of sleep in Restless Legs Syndrome(RLS) patients. A total of 303 consecutive RLS patients were assessed by self questionnaires and participant's medical records were reviewed for obtaining their clinical information. The collected data were analyzed by descriptive statistics, t-test, ANOVA, Pearson's correlation, and multiple regression. The quality of sleep was positively correlated with symptoms(r=. 21, p<.001) and depression(r=. 37, p<.001). The results of multiple regression analysis showed that significant variables influencing the quality of sleep were depression(${\beta}=.35$, p<.001), age(${\beta}=.21$, p<.001), and severity(${\beta}=.15$, p=.005). The explanation power of this regression model was 21.0% and it was statistically significant. As a result, to improve their sleep quality, the nursing interventions are required for RLS patients who have the depression, age, and severity.

• Journal of Digital Convergence
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• v.10 no.1
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• pp.413-422
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• 2012

In this study, we developed the severity-adjusted length of stay (LOS) model for acute myocardial infarction patients using data from the hospital discharge survey and proposed management of medical quality and development of policy. The dataset was taken from 2,309 database of the hospital discharge survey from 2004 to 2006. The severity-adjusted LOS model for the acute myocardial infarction (AMI) patients was developed by data mining analysis. From decision making tree model, the main reasons for LOS of AMI patients were CABG and comorbidity. The difference between severity-adjusted LOS from the ensemble model and real LOS was compared and it was confirmed that insurance type and location of hospital were statistically associated with LOS. And to conclude, hospitals should develop the severity-adjusted LOS model for frequent diseases to manage LOS variations efficiently and apply it into the medical information system.