• Clinical and Experimental Pediatrics
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• 제51권11호
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• pp.1158-1164
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• 2008

목 적: 저자들은 중환자실에 입원하는 환아들에 대한 소아사망률지표 2 (pediatric index of mortality 2, PIM2)와 소아사망위험도 III (pediatric risk of mortality III, PRISM III)의 유효성을 평가하고자 하였다. 방 법: 2003년 1월부터 2007년 12월까지 단일 기관 중환자실에 입실하여 치료받았던 환아의 의무기록을 후향적으로 조사하였다. 중환자실에 입실하여 2시간 이내에 사망하거나 절망적인 상태의 퇴원인 경우는 제외하였다. 환아들의 일반적인 특성에 대해서 Student's t-test와 ANOVA를, PIM2와 PRISM III 항목에 대해서 생존한 환아와 사망한 환아 사이에 상관분석을 시행하였다. 또한, 사망률 예측의 정도에 대한 정확성을 위해서 Hosmer-Lemeshow 적합도에 대한 다중회귀분석과 수용자 작업특성곡선을 사용하였으며 예측사망율의 과대 또는 과소 평가는 표준화된 사망비를 이용하여 검증하였다. 결 과: 193 증례의 의무기록을 검토하였으나 3예가 중환자실에 입실한 2시간이내에 사망하여 190예에 대하여 분석을 시행하였다. PIM2의 항목들은 수술이나 술기 후에 입원한 경우와 저위험군의 항목을 제외하고 생존과 연관성이 있었다. PRISM III에서는 심혈관/신경계 징후, 동맥혈가스분석의 항목이 관련성이 있었으나 생화학과 혈액학적 검사소견은 연관성이 유의하지 않았다. 수용자 작업특성곡선으로 확인한 예측도는 모두 의의가 있었으며 PIM2의 곡선하면적이 0.858 (95% 신뢰도: 0.779-0.938), PRISM III가 0.798 (95%신뢰도: 0.686-0.891)이었다. 또한, 표준화된 사망비는 두 가지 지표 모두 1에 가까웠으며 다중회귀분석을 이용한 Hosmer-Lemeshow 적합도에서 PRISM III가 ${\chi}^2(13)=12.899$, P=0.456이었으며, PIM2는 ${\chi}^2(13)=14.986$, P=0.308이었다. 그러나 PIM2가 가능도비검정에서 PRISM III보다 유의한 특성을 가지고 있었다(${\chi}^2(4)=55.3$, P<0.01). 결 론: 저자들은 중환자실에 입실하는 소아 환자에서 사망률을 예측하는 두 가지 지표(PIM2, PRISM III)가 의미가 있다는 사실을 확인하였다. 저자들은 PIM2가 PRISM III보다 보다 정확하고 적절하다고 생각된다.

• Journal of Preventive Medicine and Public Health
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• 제38권3호
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• pp.283-290
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• 2005

Objectives : The aim of this paper was to examine the relationship between the summertime (June to August) heat index, which quantifies the bioclimatic apparent temperature in sultry weather, and the daily disease-related mortality in Seoul for the period from 1991 to 2000. Methods : The daily maximum (or minimum) summertime heat indices, which show synergetic apparent temperatures, were calculated from the six hourly temperatures and real time humidity data for Seoul from 1991 to 2000. The disease-related daily mortality was extracted with respect to types of disease, age and sex, etc. and compared with the time series of the daily heat indices. Results : The summertime mortality in 1994 exceeded the normal by 626 persons. Specifically, blood circulation-related and cancer-related mortalities increased in 1994 by 29.7% (224 persons) and 15.4% (107 persons), respectively, compared with those in 1993. Elderly persons, those above 65 years, were shown to be highly susceptible to strong heat waves, whereas the other age and sex-based groups showed no significant difference in mortality. In particular, a heat wave episode on the 22nd of July 2004 ($>45^{\circ}C$ daily heat index) resulted in double the normal number of mortalities after a lag time of 3 days. Specifically, blood circulation-related mortalities, such as cerebral infraction, were predominant causes. Overall, a critical mortality threshold was reached when the heat index exceeded approximately $37^{\circ}C$, which corresponds to human body temperature. A linear regression model based on the heat indices above $37^{\circ}C$, with a 3 day lag time, accounted for 63% of the abnormally increased mortality (${\geq}+2$ standard deviations). Conclusions : This study revealed that elderly persons, those over 65 years old, are more vulnerable to mortality due to abnormal heat waves in Seoul, Korea. When the daily maximum heat index exceeds approximately $37^{\circ}C$, blood circulation-related mortality significantly increases. A linear regression model, with respect to lag-time, showed that the heat index based on a human model is a more dependable indicator for the prediction of hot weather-related mortality than the ambient air temperature.

• 대한의용생체공학회:의공학회지
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• 제42권4호
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• pp.175-185
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• 2021

Purpose: Vital sign are used to help assess the general physical health of a person, give clues to possible diseases, and show progress toward recovery. Researchers are using vital sign data and AI(artificial intelligence) to manage a variety of diseases and predict mortality. In order to analyze vital sign data using AI, it is important to select and extract vital sign data suitable for research purposes. Methods: We developed a method to visualize vital sign and early warning scores by processing retrospective vital sign data collected from EMR(electronic medical records) and patient monitoring devices. The vital sign data used for development were obtained using the open EMR big data MIMIC-III and the wearable patient monitoring device(CareTaker). Data processing and visualization were developed using Python. We used the development results with machine learning to process the prediction of mortality in ICU patients. Results: We calculated NEWS(National Early Warning Score) to understand the patient's condition. Vital sign data with different measurement times and frequencies were sampled at equal time intervals, and missing data were interpolated to reconstruct data. The normal and abnormal states of vital sign were visualized as color-coded graphs. Mortality prediction result with processed data and machine learning was AUC of 0.892. Conclusion: This visualization method will help researchers to easily understand a patient's vital sign status over time and extract the necessary data.

• Journal of Genetic Medicine
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• 제14권2호
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• pp.49-55
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• 2017

Pre-eclampsia is known to cause considerable maternal morbidity and mortality. Thus, many studies have examined the etiopathogenesis of pre-eclampsia. While many pathophysiological factors related to pre-eclampsia have been identified, the precise etiopathogenesis of pre-eclampsia remains unclear. Numerous studies have identified factors for the early prediction for pre-eclampsia to lead to preparation and closer observation on pre-eclampsia when it occurs. This article reviews on current studies of biomarkers and genetic factors related to pre-eclampsia, which may be important for developing strategies for early prediction of pre-eclampsia.

• Journal of Preventive Medicine and Public Health
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• 제47권4호
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• pp.206-215
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• 2014

Objectives: The hospital standardized mortality ratio (HSMR) has been widely used because it allows for robust risk adjustment using administrative data and is important for improving the quality of patient care. Methods: All inpatients discharged from hospitals with more than 700 beds (66 hospitals) in 2008 were eligible for inclusion. Using the claims data, 29 most responsible diagnosis (MRDx), accounting for 80% of all inpatient deaths among these hospitals, were identified, and inpatients with those MRDx were selected. The final study population included 703 571 inpatients including 27 718 (3.9% of all inpatients) in-hospital deaths. Using logistic regression, risk-adjusted models for predicting in-hospital mortality were created for each MRDx. The HSMR of individual hospitals was calculated for each MRDx using the model coefficients. The models included age, gender, income level, urgency of admission, diagnosis codes, disease-specific risk factors, and comorbidities. The Elixhauser comorbidity index was used to adjust for comorbidities. Results: For 26 out of 29 MRDx, the c-statistics of these mortality prediction models were higher than 0.8 indicating excellent discriminative power. The HSMR greatly varied across hospitals and disease groups. The academic status of the hospital was the only factor significantly associated with the HSMR. Conclusions: We found a large variation in HSMR among hospitals; therefore, efforts to reduce these variations including continuous monitoring and regular disclosure of the HSMR are required.

• 환경영향평가
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• 제20권5호
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• pp.747-755
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• 2011

This study was investigated the relationship between the temperature and the mortality of aged (${\geq}65$ yr) during the winter seasons from 1992 to 2007 in Seoul, Korea by utilizing climate data and death records. The study also estimated the future risks by employing the projections of the population in Seoul, Korea and climate change scenario of Korea from 2011 to 2030. The limitation of this study was the impossibility in the prediction of daily mortality counts. Therefore, daily death numbers could be predicted based on the future population projection for Korea and the death records of 2005. The result indicated that risks increased by 0.27%, 0.52%, 0.32% and 0.41% in association with the $1^{\circ}C$ decrease in daily minimum temperature from the mortality counts of total, respiratory, cardiovascular, and cardiorespiratory in the past date while 0.31%, 0.42%, 0.59% and 0.66% in the future. Based on the results obtained from this study, it is concluded that the risk in the future will be higher than the past date although there is an uncertainty in estimating death counts in the future.

• 대한임상독성학회지
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• 제13권2호
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• pp.55-61
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• 2015

Purpose: Many studies have examined the mechanisms of impaired glucose homeostasis after organophosphate (OP) exposure, however no study has evaluated the clinical utility of blood glucose measurements in patients with OP poisoning. The current study was conducted to evaluate the initial glucose level at presentation and the glycemic variables during the first 3 days after admission as a predictor of mortality. Methods: This retrospective observational case series included 228 patients with a history of OP poisoning. Among other clinical data, information on the initial glucose level at presentation and mean glucose level, delta glucose level, and the presence of a hypoglycemic event during the first 3 days of admission, was collected. Results: Survivors had lower initial glucose levels at presentation and glucose variability during the first 3 days of admission compared to non-survivors. The frequency of hypoglycemic events was higher in non-survivors. In multivariate analysis, the initial glucose level (> 233 mg/dl) was an independent predictor of mortality, along with age. Conclusion: The initial glucose level at presentation can be helpful in prediction of mortality in cases of OP intoxication at bedside. The physician should pay attention to patients with a glucose level >233 mg/dl at presentation after ingestion of OP.

• Asian Pacific Journal of Cancer Prevention
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• 제16권10호
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• pp.4237-4245
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• 2015

Breast cancer is a menacing cancer, primarily affecting women. Continuous research is going on for detecting breast cancer in the early stage as the possibility of cure in early stages is bright. There are two main objectives of this current study, first establish statistics for breast cancer and second to find methodologies which can be helpful in the early stage detection of the breast cancer based on previous studies. The breast cancer statistics for incidence and mortality of the UK, US, India and Egypt were considered for this study. The finding of this study proved that the overall mortality rates of the UK and US have been improved because of awareness, improved medical technology and screening, but in case of India and Egypt the condition is less positive because of lack of awareness. The methodological findings of this study suggest a combined framework based on data mining and evolutionary algorithms. It provides a strong bridge in improving the classification and detection accuracy of breast cancer data.

• 대기
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• 제18권4호
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• pp.355-365
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• 2008

Synoptic and climatological characteristics of heat waves over Korea and Europe as well as their biometeorological impacts were compared. In July of 1994, excess deaths of about 2,388 in the population of South Korea are estimated by the modified excess death calculation algorithm ofKysely (2004). The excess deaths correspond to the net mortality increase of 12.5% in July of 1994 if we compare the estimated value to the expected number of deaths in this month (i.e., about 19,171). The comparative study of heat waves in Korea and Europe shows that the record-breaking heat waves in both regions are closely associated with prolonged droughts. In particular, reduction of soil moisture, precipitation and cloud cover and enhancement of insolation during the drought periods are very likely to be related to the increase in the intensity and the duration ofheat waves. Climate models predict that the frequency, intensity, and duration of heat waves in the 21 st century will be greatly enhanced in both areas. In order to reduce the biometeorological and socioeconomic impacts due to heat waves, not only the development of heat-related mortality prediction model that can be widely applied to many climate regimes, but also studies on the climatological association between extreme temperatures and abnormal hydrological cycle are needed.

• 한국인공지능학회지
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• 제11권3호
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).