• Parasites, Hosts and Diseases
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• v.55 no.2
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• pp.167-174
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• 2017

China still has more than 30,000 patients of advanced schistosomiasis while new cases being reported consistently. D-dimer is a fibrin degradation product. As ascites being the dominating symptom in advanced schistosomiasis, the present study aimed to explore a prediction model of ascites with D-dimer and other clinical easy-achievable indicators. A case-control study nested in a prospective cohort was conducted in schistosomiasis-endemic area of southern China. A total of 291 patients of advanced schistosomiasis were first investigated in 2013 and further followed in 2014. Information on clinical history, physical examination, and abdominal ultrasonography, including the symptom of ascites was repeatedly collected. Result showed 44 patients having ascites. Most of the patients' ascites were confined in the kidney area with median area of $20mm^2$. The level of plasma D-dimer and pertinent liver function indicators were measured at the initial investigation in 2013. Compared with those without ascites, cases with ascites had significantly higher levels of D-dimer ($0.71{\pm}2.44{\mu}g/L$ vs $0.48{\pm}2.12{\mu}g/L$, P=0.005), as well ALB (44.5 vs 46.2, g/L) and Type IV collagen (50.04 vs $44.50{\mu}g/L$). Receiver operating characteristic curve analyses indicated a moderate predictive value of D-dimer by its own area under curve (AUC) of 0.64 (95% CI: 0.54-0.73) and the cutoff value as $0.81{\mu}g/L$. Dichotomized by the cutoff level, D-dimer along with other categorical variables generated a prediction model with AUC of 0.76 (95% CI: 0.68-0.89). Risks of patients with specific characteristics in the prediction model were summarized. Our study suggests that the plasma D-dimer level is a reliable predictor for incident ascites in advanced schistosomiasis japonica patients.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.12
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• pp.6187-6190
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• 2012

We aimed to investigate DNA repair gene expression of response to chemotherapy among gastric patients, and roles in the prognosis of gastric cancer. A total of 209 gastric cancer patients were included in this study between January 2007 and December 2008, all treated with chemotherapy. Polymorphisms were detected by real time PCR with TaqMan probes, and genomic DNA was extracted from peripheral blood samples. The overall response rate was 61.2%. The median progression and overall survivals were 8.5 and 18.7 months, respectively. A significant increased treatment response was found among patients with XPG C/T+T/T or XRCC1 399G/A+A/A genotypes, with the OR (95% CI) of 2.14 (1.15-4.01) and 1.75 (1.04-3.35) respectively. We found XPG C/T+T/T and XRCC1 399 G/A+A/A were associated with a longer survival among gastric cancer patients when compared with their wide type genotypes, with HRs and 95% CIs of 0.49 (0.27-0.89) and 0.56 (0.29-0.98) respectively. Selecting specific chemotherapy based on pretreatment genotyping may be an innovative strategy for further studies.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.4
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• pp.274-283
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• 2005

Coronary Perfusion Pressure(CPP) is known for the most important parameter related to the Return of Spontaneous Circulation (ROSC), however, clinically measuring CPP is difficult either invasive or non-invaisive method. En this paper, we analyze the correlation between the extracted parameter from VF ECG wave and the CPP with the statistical method, and predict CPP value using the extracted parameters within significance level. the extracted parameters are median frequency(MF), peak frequency(PF), average segment amplitude(ASA), MSA(maximum segment amplitude), Two parameters, MF, and ASA are selected in order to predict CPP value with general regression neural network, and then we evaluated the agreement statistics between the simulated CPP and the measured CPP. In conclusion, the mean and variance of the difference between the simulated CPP and the measured CPP are 8.9716±1.3526 mmHg, and standard deviation 6.4815 mmHg with one hundred-times training and test results. the simulated CPP and the measured CPP are agreed with the overall accuracy $90.68\%$ and kappa coefficient $81.14\%$ as a discriminant parameter of ROSC.

• Journal of Distribution Science
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• v.19 no.11
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• pp.49-57
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• 2021

Purpose: In this study, we try to figure out the appropriate size of commercial districts in the original downtown area through empirical studies targeting the Jinju Central Commercial Area in Gyeongnam and Cheonan Station in Chungnam, which are trying to regenerate a specific space that has been lost through government projects. Research design, data and methodology: The current status and characteristics of the shopping district were examined through on-site surveys of the central business district of Jinju, Gyeongnam Province, and Cheonan Station, Chungnam Province, and the size of the empty stores was determined. In addition, the standard median income was used as the survey data along with the survey of the mobile population in the commercial area. Result: The analysis result shows that 883 stores should be maintained considering the overall expenditure and gross sales profit within Cheonan Station in South Chungcheong Province. Currently, considering spending and margins in the Commercial Area, Jinju Central Commercial Area is a place where 222 stores can be sold excessively, and a proper commercial supply plan is needed. Conclusions: In this study, we conducted a demand prediction study in the commercial sector of the most basic sector to regenerate the commercial sector through major regional commercial districts.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.47-47
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• 2023

Drought occurs due to lack of water resources over an extended period and its intensity has been magnified globally by climate change. In recent years, drought over South Korea has also been intensed, and the prediction was inevitable for the water resource management and water industry. Therefore, drought forecasting over South Korea was performed in the current study with the following procedure. First, accumulated spring precipitation(ASP) driven by the 93 weather stations in South Korea was taken with their median. Then, correlation analysis was followed between ASP and Df4m, the differences of two pair of the global winter MSLP. The 37 Df4m variables with high correlations over 0.55 was chosen and sorted into three regions. The selected Df4m variables in the same region showed high similarity, leading the multicollinearity problem. To avoid this problem, a model that performs variable selection and model fitting at once, least absolute shrinkage and selection operator(LASSO) was applied. The LASSO model selected 5 variables which showed a good agreement of the predicted with the observed value, R²=0.72. Other models such as multiple linear regression model and ElasticNet were also performed, but did not present a performance as good as LASSO. Therefore, LASSO model can be an appropriate model to forecast spring drought over South Korea and can be used to mange water resources efficiently.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.149-163
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• 2023

Affected by external factors, errors in time series data collected by sensors are common. Using the traditional method of constraining the speed change rate to clean the errors can get good performance. However, they are only limited to the data of stable changing speed because of fixed constraint rules. Actually, data with uneven changing speed is common in practice. To solve this problem, an online cleaning algorithm for time series data based on dynamic speed change rate constraints is proposed in this paper. Since time series data usually changes periodically, we use the extreme learning machine to learn the law of speed changes from past data and predict the speed ranges that change over time to detect the data. In order to realize online data repair, a dual-window mechanism is proposed to transform the global optimal into the local optimal, and the traditional minimum change principle and median theorem are applied in the selection of the repair strategy. Aiming at the problem that the repair method based on the minimum change principle cannot correct consecutive abnormal points, through quantitative analysis, it is believed that the repair strategy should be the boundary of the repair candidate set. The experimental results obtained on the dataset show that the method proposed in this paper can get a better repair effect.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2266-2273
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• 2024

A machine learning platform is proposed for the diagnosis of a severe accident progression in a nuclear power plant. To predict the key parameters for accident management including lost signals, a long short term memory (LSTM) network is proposed, where multiple accident scenarios are used for training. Training and test data were produced by MELCOR simulation of the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident at unit 3. Feature variables were selected among plant parameters, where the importance ranking was determined by a recursive feature elimination technique using RandomForestRegressor. To answer the question of whether a reduced order ML model could predict the complex transient response, we performed a systematic sensitivity study for the choices of target variables, the combination of training and test data, the number of feature variables, and the number of neurons to evaluate the performance of the proposed ML platform. The number of sensitivity cases was chosen to guarantee a 95 % tolerance limit with a 95 % confidence level based on Wilks' formula to quantify the uncertainty of predictions. The results of investigations indicate that the proposed ML platform consistently predicts the target variable. The median and mean predictions were close to the true value.

• Childhood Kidney Diseases
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• v.4 no.2
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• pp.144-153
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• 2000

Purpose : Effects to predict tile progression of chronic renal failure (CRF) in children, using mathematical models based on transformations of serum creatinine (Scr) concentration, have failed. Error may be introduced by age-related variations in creatinine production rate. Height (Ht) is a reliable reference for creatinine production in children. Thus, Scr, factored for Ht, could provide a more accurate predictive model. We examined this hypothesis. Methods : The progression of of was detected in 63 children who proceeded to end-stage renal disease. Derivatives of Scr, including 1/Scr, log Scr & Ht/Scr, were defined fir the period Scr was between 2 and 5 mg/dl. Regression equation were used to predict the time, in months, to Scr > 10 mg/dl. The prediction error (PE) was defined as the predicted time minus actual time for each Scr transformation. Result : The PE for Ht/Scr was lower than the PE for either 1/Scr or log Scr (median: -0.01, -2.0 & +10.6 mos respectively; P<0.0001). For children with congenital renal diseases, the PE for Ht/Scr was also lower than for the other two transformations (median: -1.2, -3.2 & +8.2 mos respectively; P<0.0001). However, the PEs for children with glomerular diseases was not as clearly different (median: +0.9, +0.5 & +9.9 respectively). In children < 13 yrs, PE for Ht/Scr was tile lowest, while in older children, 1/Scr provided the lowest PE but not significantly different from that for Ht/Scr. The logarithmic transformation tended to predict a slower progression of CRF than actually occurred. Conclusion : Scr, floored for Ht, appears to be a useful model to predict the rate of progression of CRF, particularly in the prepubertal child with congenital renal disease.

• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.125-136
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• 2005

The current traffic accident reduction procedure in economic feasibility study does not consider the characteristics of road and V/C ratio. For solving this problem, this paper suggests methods to be able to evaluate safety of each road in construction and improvement through developing accident Prediction model in reflecting V/C ratio Per road types and traffic characters. In this paper as primary process, model is made by tke object of urban roads. Most of all, factor effecting on accident relying on road types is selected. At this point, selecting criteria chooses data obtained from road planning procedure, traffic volume, existence or non-existence of median barrier, and the number of crossing point, of connecting road. and of traffic signals. As a result of analyzing between each factor and accident. all appear to have relatives at a significant level of statistics. In this research, models are classified as 4-categorized classes according to roads and V/C ratio and each of models draws accident predicting model through Poisson regression along with verifying real situation data. The results of verifying models come out relatively satisfactory estimation against real traffic data. In this paper, traffic accident prediction is possible caused by road's physical characters by developing accident predicting model per road types resulted in V/C ratio and this result is inferred to be used on predicting accident cost when road construction and improvement are performed. Because data using this paper are limited in only province of Jeollabuk-Do, this paper has a limitation of revealing standards of all regions (nation).

• Journal of Gastric Cancer
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• v.22 no.2
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• pp.120-134
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• 2022

Purpose: This study aimed to identify prognostic factors for patients with distant lymph node-involved gastric cancer (GC) using a machine learning algorithm, a method that offers considerable advantages and new prospects for high-dimensional biomedical data exploration. Materials and Methods: This study employed 79 features of clinical pathology, laboratory tests, and therapeutic details from 289 GC patients whose distant lymphadenopathy was presented as the first episode of recurrence or metastasis. Outcomes were measured as any-cause death events and survival months after distant lymph node metastasis. A prediction model was built based on possible outcome predictors using a random survival forest algorithm and confirmed by 5×5 nested cross-validation. The effects of single variables were interpreted using partial dependence plots. A contour plot was used to visually represent survival prediction based on 2 predictive features. Results: The median survival time of patients with GC with distant nodal metastasis was 9.2 months. The optimal model incorporated the prealbumin level and the prothrombin time (PT), and yielded a prediction error of 0.353. The inclusion of other variables resulted in poorer model performance. Patients with higher serum prealbumin levels or shorter PTs had a significantly better prognosis. The predicted one-year survival rate was stratified and illustrated as a contour plot based on the combined effect the prealbumin level and the PT. Conclusions: Machine learning is useful for identifying the important determinants of cancer survival using high-dimensional datasets. The prealbumin level and the PT on distant lymph node metastasis are the 2 most crucial factors in predicting the subsequent survival time of advanced GC.