• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.4
• /
• pp.1863-1870
• /
• 2013

The purpose of this study is when the cases will be found, used as a basic data for clinical severity prediction, and research on suicide prevention. By classifying the group of survival and death about the patients who visit the Emergency Medical Center by attempt suicide by drug addiction, identifying the condition when visiting and results of the treatment after visiting. From June 2009 to May 2011, last two years data that among the drug abusers who visited the Emergency Medical Center in C-University Hospital in Gwang-Ju, only suicidal patients, except with unintentional accidents were collected. The findings, among the drug addiction patients who high age, lower level of education and living alone were the mortality rate was higher. And if who drunk the agricultural chemicals, the convalescence was not good. If the causes of suicide were economic problems and depression, the mortality rate was higher. And when visit hospital, if the consciousness was stupor and semi-coma/coma, the convalescence was not good. As grasp the risk for suicide patients of drug addiction, help on the Prediction of clinical severity, also stamp the appropriate drug education with psychological support is more important on them.

• Journal of Korean Society of Disaster and Security
• /
• v.13 no.2
• /
• pp.27-38
• /
• 2020

Climate variations have become worse and diversified recently, which caused catastrophic disasters for our communities and ecosystem including economic property damages in Korea. Heat wave of summer season is one of causes for such damages of which outbreak tends to increase recently. Related short-term forecasting information has been provided by the Korea Meteorological Administration based on results from numerical forecasting model. As the study area, the ◯◯ province was selected because of the highest mortality rate in Korea for the past 15 years (1998~2012). When comparing the forecasted temperatures with field measurements, it showed RMSE of 1.57℃ and RMSE of 1.96℃ was calculated when only comparing the data corresponding to the observed value of 33℃ or higher. The forecasting process would take at least about 3~4 hours to provide the 4 hours advanced forecasting information. Therefore, this study proposes a methodology for temperature prediction using LSTM considering the short prediction time and the adequate accuracy. As a result of 4 hour temperature prediction using this approach, RMSE of 1.71℃ was occurred. When comparing only the observed value of 33℃ or higher, RMSE of 1.39℃ was obtained. Even the numerical prediction model of the whole range of errors is relatively smaller, but the accuracy of prediction of the machine learning model is higher for above 33℃. In addition, it took an average of 9 minutes and 26 seconds to provide temperature information using this approach. It would be necessary to study for wider spatial range or different province with proper data set in near future.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.2
• /
• pp.137-148
• /
• 2011

In this paper, we systematically investigate regional differences of the dead due to injuries in cities, towns and counties about transportation accidents, suicides and fall accidents, which have recently been an important issue of health problems in Korea, The data are from the Annual Report on the Cause of Death Statistics in Korea in 2008. They include the deaths over the age 19 from transportation accidents, suicides and fall accidents with the criterion of the International Statistical Classification of Diseases. Poisson HGLM is applied to estimate the mortality rate under the assumption that the number of deaths follow a Poisson distribution, by considering regions as random effects and by adjusting age, sex and standardized residence tax as fixed effects. Using the results of random effects prediction, the regional differences in cities, counties and towns are marked in disease mapping. The results showed that there were significant regional differences of mortality rates for transportation accidents and suicides, but no significant differences for fall accidents.

• Korean Journal of Artificial Intelligence
• /
• v.12 no.1
• /
• pp.25-29
• /
• 2024

In this study, research was conducted to predict the probability of cervical cancer occurrence associated with the use of hormonal contraceptives. Cervical cancer is influenced by various environmental factors; however, the human papillomavirus (HPV) is detected in 99% of cases, making it the primary attributed cause. Additionally, although cervical cancer ranks 10th in overall female cancer incidence, it is nearly 100% preventable among known cancers. Early-stage cervical cancer typically presents no symptoms but can be detected early through regular screening. Therefore, routine tests, including cytology, should be conducted annually, as early detection significantly improves the chances of successful treatment. Thus, we employed artificial intelligence technology to forecast the likelihood of developing cervical cancer. We utilized the logistic regression algorithm, a predictive model, through Microsoft Azure. The classification model yielded an accuracy of 80.8%, a precision of 80.2%, a recall rate of 99.0%, and an F1 score of 88.6%. These results indicate that the use of hormonal contraceptives is associated with an increased risk of cervical cancer. Further development of the artificial intelligence program, as studied here, holds promise for reducing mortality rates attributable to cervical cancer.

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

• Neonatal Medicine
• /
• v.18 no.2
• /
• pp.221-227
• /
• 2011

Purpose: The objectives were to identify the characteristics of neonates with hydrops fetalis, and to identify the risk factors associated with mortality. Methods: A retrospective review of AMC (Asan Medical Center) dataset was performed from January 1990 to June 2009. The characteristics of 71 patients with hydrops fetalis were investigated and they were divided into two groups: the survived group and the expired group. Various perinatal and neonatal factors in two groups were compared to find out risk factors associated with mortality based on univariate analysis, followed by multiple regression analyses (SPSS version 18.0). Results: Of those 71 neonates (average gestational age: 33 weeks, birth weight: 2.6 kg), 38 survived, 33 died, resulting in overall mortality rate of 46.5%. The most common etiology was idiopathic followed by chylothorax, cardiac anomalies, twin-to-twin transfusion, meconium peritonitis, cardiac arrythmias, and congenital infections. Factors that were associated independently with mortality in logistic regression analyses were low 5-minutes Apgar score, hyaline membrane disease and delayed in achieving 50th percentile ideal body weight for appropriate gestational age by 10 days. Conclusion: In this study, 5-minutes Apgar score, hyaline membrane disease and delayed in achieving 50th percentile ideal body weight for appropriate gestational age by 10 days were significant risk factors associated with mortality in hydrops fetalis. Therefore, the risk of death among neonates with hydrops fetalis depends on the illness immediately after birth and severity of hydrops fetalis. Informations from this study may prove useful in prediction of prognosis to neonates with hydrops fetalis.

• Journal of Yeungnam Medical Science
• /
• v.38 no.4
• /
• pp.337-343
• /
• 2021

Background: Chromogranin A (CgA) levels have been reported to predict mortality in patients with heart failure. However, information on the prognostic value and clinical availability of CgA is limited. We compared the prognostic value of CgA to that of previously proven natriuretic peptide biomarkers in patients with acute heart failure. Methods: We retrospectively evaluated 272 patients (mean age, 68.5±15.6 years; 62.9% male) who underwent CgA test in the acute stage of heart failure hospitalization between June 2017 and June 2018. The median follow-up period was 348 days. Prognosis was assessed using the composite events of 1-year death and heart failure hospitalization. Results: In-hospital mortality rate during index admission was 7.0% (n=19). During the 1-year follow-up, a composite event rate was observed in 12.1% (n=33) of the patients. The areas under the receiver-operating characteristic curves for predicting 1-year adverse events were 0.737 and 0.697 for N-terminal pro-B-type natriuretic peptide (NT-proBNP) and CgA, respectively. During follow-up, patients with high CgA levels (>158 pmol/L) had worse outcomes than those with low CgA levels (≤158 pmol/L) (85.2% vs. 58.6%, p<0.001). When stratifying the patients into four subgroups based on CgA and NT-proBNP levels, patients with high NT-proBNP and high CgA had the worst outcome. CgA had an incremental prognostic value when added to the combination of NT-proBNP and clinically relevant risk factors. Conclusion: The prognostic power of CgA was comparable to that of NT-proBNP in patients with acute heart failure. The combination of CgA and NT-proBNP can improve prognosis prediction in these patients.

• Asian-Australasian Journal of Animal Sciences
• /
• v.18 no.6
• /
• pp.780-786
• /
• 2005

This study presents a non-video, non-invasive, automatic, on-site monitoring system the system employs ultrasonic transducers to detect behavior in sows before, during and after parturition. An ultrasonic transmitting/receiving (T/R) circuit of 40 kHz was mounted above a conventional parturition bed. The T/R units use ultrasonic time-of-flight (TOF) ranging technology to measure the height of the confined sows at eight predetermined locations. From this data, three momentary postures of the sow are determined, characterized as standing-posture (SP), lateral-lying-posture (LLP) and sitting posture (STP). By examining the frequencies of position switch Stand-Up-Sequence (SUS) between standing-posture (SP), lateral-lying-posture (LLP) and sitting-posture (STP) rate can be determined for the duration of the sow' confinement. Three experimental pureblooded Landrace sows undergoing normal gestation were monitored for the duration of confinement. In agreement with common observation, the sows exhibited increased restlessness as parturition approached. Analysis of the data collected in our study showed a distinct peak in Stand-Up-Sequence (SUS, i.e. the transition from lying laterally to standing up ) and sitting-posture (STP) rate approximately 12 h prior to parturition, the observed peak being 5 to 10 times higher than observed on any other measurement day. It is concluded that the presented methodology is a robust, low-cost, lowlabor method for the continuous remote monitoring of sows and similar large animals for parturition and other behavior. It is suggested that the system could be applied to automatic prediction of sow parturition, with automatic notification of remote management personnel so human attendance at birth could reduce rates of sow and piglet mortality. The results of this study provide a good basis for enhancing automation and reducing costs in large-scale sow husbandry and have applications in the testing of various large mammals for the effects of medications, diets, genetic modifications and environmental factors.

• Journal of Chest Surgery
• /
• v.55 no.2
• /
• pp.143-150
• /
• 2022

Background: The effectiveness of extracorporeal membrane oxygenation (ECMO) for patients with refractory cardiogenic shock or cardiac arrest is being established, and serum lactate is well known as a biomarker of end-organ perfusion. We evaluated the efficacy of pre-ECMO lactate for predicting 6-month survival in patients with acute coronary syndrome (ACS) undergoing ECMO. Methods: We reviewed the medical records of 148 patients who underwent veno-arterial (VA) ECMO for ACS between January 2015 and June 2020. These patients were divided into survivors and non-survivors based on 6-month survival. All clinical data before and during ECMO were compared between the 2 groups. Results: Patients' mean age was 66.0±10.5 years, and 116 (78.4%) were men. The total survival rate was 45.9% (n=68). Cox regression analysis showed that the pre-ECMO lactate level was an independent predictor of 6-month mortality (hazard ratio, 1.210; 95% confidence interval [CI], 1.064-1.376; p=0.004). The area under the receiver operating characteristic curve of pre-ECMO lactate was 0.64 (95% CI, 0.56-0.72; p=0.002; cut-off value=9.8 mmol/L). Kaplan-Meier survival analysis showed that the cumulative survival rate at 6 months was significantly higher among patients with a pre-ECMO lactate level of 9.8 mmol/L or less than among those with a level exceeding 9.8 mmol/L (57.3% vs. 31.8%, p=0.0008). Conclusion: A pre-ECMO lactate of 9.8 mmol/L or less may predict a favorable outcome at 6 months in ACS patients undergoing VA-ECMO. Further research aiming to improve the accuracy of predictions of reversibility in patients with high pre-ECMO lactate levels is essential.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.5
• /
• pp.21-28
• /
• 2022

All health problems that occur in the circulatory system are refer to cardiovascular illness, such as heart and vascular diseases. Deaths from cardiovascular disorders are recorded one third of in total deaths in 2019 worldwide, and the number of deaths continues to rise. Therefore, if it is possible to predict diseases that has high mortality rate with patient's data and AI system, they would enable them to be detected and be treated in advance. In this study, models are produced to predict heart disease, which is one of the cardiovascular diseases, and compare the performance of models with Accuracy, Precision, and Recall, with description of the way of improving the performance of the Decision Tree(Decision Tree, KNN (K-Nearest Neighbor), SVM (Support Vector Machine), and DNN (Deep Neural Network) are used in this study.). Experiments were conducted using scikit-learn, Keras, and TensorFlow libraries using Python as Jupyter Notebook in macOS Big Sur. As a result of comparing the performance of the models, the Decision Tree demonstrates the highest performance, thus, it is recommended to use the Decision Tree in this study.