Information Processing in Design and Testing of Financial Early Warning Method of Listed Companies in China - Based on the Logistic Regression Analysis

2014 ◽  
Vol 1022 ◽  
pp. 325-328 ◽  
Author(s):  
Yu Mei He ◽  
Meng Li ◽  
Xin Yi Xu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Information Processing ◽  
Logistic Regression Analysis ◽  
Early Warning ◽  
Nonparametric Test ◽  
Listed Companies ◽  
Quadratic Term ◽  
Logistic Analysis ◽  
Financial Index

Information processing is of essential importance for an effective financial early warning model. In order to design the model of financial early warning for listed companies, we selected the 120 listed companies as research samples, chose the necessary information and relatively financial indexes and detected them by K-S normality test, then separately analyzed the results by One-Sample T test and Nonparametric Test. Through the discriminant analysis to the correlation analysis, we can identify the independent variables of logistic analysis, introduce in the quadratic term and cross term for logistic regression analysis. Applied the model in financial crisis prediction of the listed companies in China, the empirical results indicate that the prediction accuracy is 87.4%. The model from relatively information and financial index provides a kind of early warning method, which is simple practicable and scientific, for estimating the business status of a company.

Increased pretreatment C-reactive protein-to-albumin ratio predicts severe coronavirus disease 2019

2020 ◽  
Author(s):  
Xiaoyue Wang ◽  
Yan Xu ◽  
Huang Huang ◽  
Desheng Jiang ◽  
Chunlei Zhou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Early Warning ◽  
Warning Signs ◽  
C Reactive Protein ◽  
Albumin Ratio ◽  
Reactive Protein ◽  
Independent Risk Factors

Abstract Objective The aim of this study was to identify early warning signs for severe coronavirus disease 2019 (COVID-19). Methods We retrospectively analysed the clinical data of 90 patients with COVID-19 from Guanggu District of Hubei Women and Children Medical and Healthcare Center, comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. The cutoff values were determined by receiver operating characteristic curve analysis. Logistic regression analysis was performed to identify the independent risk factors for severe COVID-19. Results The patients with mild and severe COVID-19 had significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), and pretreatment C-reactive protein-to-albumin ratio (CAR) ( P =0.000; P =0.008; P=0.000; P =0.000). The severity of COVID-19 was positively correlated with comorbid cancer, age, NLR, and CAR ( P <0.005). Multivariate logistic regression analysis showed that age, the NLR and the CAR were independent risk factors for severe COVID-19 (OR=1.086, P =0.008; OR=1.512, P =0.007; OR=17.652, P =0.001). Conclusion An increased CAR can serve as an early warning sign of severe COVID-19 in conjunction with the NLR and age.

scholarly journals Combining Option Approach with Logistic Regression Analysis to Measure Default Risk of Listed Companies on Vietnamese Stock Market

2013 ◽  
Vol 217 ◽  
pp. 92-109
Author(s):  
ANH PHAN ĐÌNH ◽  
NHÂN NGUYỄN HÒA
Keyword(s):  
Economic Growth ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Stock Market ◽  
Causal Relationship ◽  
Logistic Regression Analysis ◽  
Default Risk ◽  
Causal Relation ◽  
Listed Companies

This paper explores correlation between growth of stock market and economic growth in Vietnam by examining causal relationship between VN-Index and the GDP. The results show that there is no evidence of a causal relation between VN-Index and economic growth. This finding allows several implications of restructuring of the Vietnamese economy.

An increased pretreatment C-reactive protein-to-albumin ratio predicts severe novel coronavirus-infected pneumonia

2020 ◽  
Author(s):  
Xiaoyue Wang ◽  
Yan Xu ◽  
Huang Huang ◽  
Desheng Jiang ◽  
Chunlei Zhou ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Early Warning ◽  
Multivariate Logistic Regression Analysis ◽  
Warning Signs ◽  
C Reactive Protein ◽  
Albumin Ratio ◽  
Reactive Protein ◽  
Novel Coronavirus

Abstract Objective The aim of this study was to identify early warning signs for severe novel coronavirus-infected pneumonia (COVID-19). Methods We retrospectively analyzed the clinical data of 90 patients with COVID-19 from Guanggu District of Hubei Women and Children Medical and Healthcare Center comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. The cutoff values was determined by receiver operating characteristic curve analysis. Logistic regression analysis was performed to identify the independent risk factor that predicted the severe COVID-19. Results The patients with mild and severe COVID-19 showed significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), and pretreatment C-reactive protein-to-albumin ratio (CAR) (P < 0.05). The severity of COVID-19 was correlated positively with the comorbidity of cancer, age, NLR, and CAR (P < 0.05). Multivariate logistic regression analysis showed that age, NLR and CAR were independent risk factors for severe COVID-19 (OR = 1.086, P = 0.008; OR = 1.512, P = 0.007; OR = 17.652, P = 0.001, respectively). Conclusion An increased CAR can serve as an early warning sign of severe COVID-19 in conjunction with the NLR and age.

Relationship between pharmacokinetics and safety of tremelimumab in patients with melanoma

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 3049-3049
Author(s):  
E. Wang ◽  
D. Kang ◽  
D. Wang ◽  
C. Bulanhagui ◽  
P. Hsyu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Common Adverse Event ◽  
Advanced Melanoma ◽  
Mixed Effect ◽  
Logistic Analysis ◽  
Nonlinear Mixed Effect ◽  
Nonlinear Mixed Effect Modeling ◽  
Auc Value

3049 Background: Tremelimumab is a fully human anti-CTLA4 monoclonal antibody that has antitumor activity in some patients with advanced melanoma. The most common adverse event associated with tremelimumab is diarrhea/colitis. This analysis investigated the relationship between pharmacokinetics (PK) and diarrhea or colitis in 450 patients enrolled in four phase I or II clinical studies. Methods: PK parameters including AUC during the first 90 days (AUC90), Cmax, concentration at day 29 (C29), and AUC of concentration >30 μg/mL (AUCG30) of tremelimumab were determined using nonlinear mixed-effect modeling. Severity and frequency of diarrhea and colitis during the 90 days after the first dose were considered in relation to each individual PK parameter. Frequency of diarrhea was further analyzed by logistic regression analysis. Frequencies of diarrhea and colitis were combined for logistic analysis. Results: The PK/safety analysis included 450 patients (445 with advanced melanoma). In this analysis, the severity of diarrhea and colitis appeared to be independent of PK (AUC90, Cmax, C29, and AUCG30) of tremelimumab. Although frequency of diarrhea was positively related to AUC (AUC or AUCG30), Cmax, and C29, AUC appeared to be best correlated to frequency of diarrhea. Logistic regression analysis of change in AUC on probability of having diarrhea indicated that the frequency of diarrhea increased initially with increasing AUC, but reached a plateau quickly at approximately AUC value of 50,000 μg × hr/mL (20th percentile). Results of the combined diarrhea and colitis analysis were similar to those of diarrhea alone. Conclusions: Severity of diarrhea and colitis is independent of AUC in patients with melanoma treated with tremelimumab. Frequency of diarrhea and colitis initially increases with increasing AUC, but plateaus quickly. [Table: see text]

scholarly journals An increased pretreatment neutrophil-to-lymphocyte ratio predicts severe novel coronavirus-infected pneumonia

2020 ◽  
Author(s):  
Xiaoyue Wang ◽  
Desheng Jiang ◽  
Huang Huang ◽  
Xiaofeng Chen ◽  
Chunlei Zhou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Early Warning ◽  
Roc Curve ◽  
Neutrophil To Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Independent Risk Factors ◽  
Novel Coronavirus

Abstract Objective The aim of this study was to identify early warning signs for severe novel coronavirus-infected pneumonia (COVID-19).Methods We retrospectively analyzed the clinical data of 90 patients with COVID-19 at the Guanggu District of Hubei Women and Children Medical and Healthcare Center comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. Logistic regression analysis was performed to identify the independent risk factors that predicted severe COVID-19. The receiver-operating characteristic (ROC) curve of independent risk factors was calculated, and the area under the curve (AUC) was used to evaluate the efficiency of the prediction of severe COVID-19.Results The patients with mild and severe COVID-19 showed significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP) and the serum albumin (ALB) level (P<0.05). The severity of COVID-19 was correlated positively with the comorbidity of cancer, age, NLR, and CRP but was negatively correlated with the ALB level (P<0.05). Multivariate logistic regression analysis showed that the NLR and ALB level were independent risk factors for severe COVID-19 (OR=1.319, 95% CI: 1.043-1.669, P=0.021; OR=0.739, 95% CI: 0.616-0.886, P=0.001), with AUCs of 0.851 and 0.128, respectively. An NLR of 4.939 corresponded to the maximum joint sensitivity and specificity according to the ROC curve (0.700 and 0.917, respectively).Conclusion An increased NLR can serve as an early warning sign of severe COVID-19.

ANALISIS HUBUNGAN SARANA, PENGETAHUAN, DAN SIKAP BIDAN PRAKTIK MANDIRI DENGAN KEPATUHAN MENJALANKAN STANDAR PELAYANAN ANTENATAL CARE DI KOTA PADANGSIDIMPUAN

2019 ◽  
Vol 2 (1) ◽  
pp. 27-33
Author(s):  
Megawati Sinambela ◽  
Evi Erianty Hasibuan
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Antenatal Care ◽  
Pregnant Women ◽  
Logistic Regression Analysis ◽  
Care Service ◽  
Multivariate Logistic Regression Analysis ◽  
Bivariate Analysis ◽  
Cross Sectional ◽  
The North

Antenatal care is a service provided to pregnant women to monitor, support maternal health and detect mothers whether normal or problematic pregnant women. According to the WHO, globally more than 70% of maternal deaths are caused by complications of pregnancy and childbirth such as hemorrhage, hypertension, sepsis, and abortion. Based on data obtained from the profile of the North Sumatra provincial health office in 2017, in the city of Padangsidimpuan in 2017 the coverage of ANC visits reached (76.58%) and had not reached the target in accordance with the 2017 Provincial Health Office strategy plan (95%). This type of research was an observational analytic study with a cross sectional design. The population in this study were independent practice midwives who were in the Padangsidimpuan, the sample in this study amounted to 102 respondents. The technique of collecting data used questionnaires and data analysis used univariate, bivariate and multivariate analysis with logistic regression analysis. Based on bivariate analysis showed that there was a relationship between facilities, knowledge and attitudes of independent midwives with compliance with the standards of antenatal care services with a value of p <0.05. The results of the study with multivariate logistic regression analysis showed that the factors associated with the compliance of independent midwives in carrying out antenatal care service standards were attitudes with values (p = 0.026).

scholarly journals Efficacy of flurbiprofen axetil for preventing postanesthetic shivering in patients undergoing gynecologic laparotomy surgeries

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Atsushi Kotera
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Postoperative Pain ◽  
Logistic Regression Analysis ◽  
Pain Control ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Postoperative Pain Control ◽  
Flurbiprofen Axetil ◽  
Postanesthetic Shivering

Abstract Background Postanesthetic shivering is an unpleasant adverse event in surgical patients. A nonsteroidal anti-inflammatory drug has been reported to be useful in preventing postanesthetic shivering in several previous studies. The aim of this study was to evaluate the efficacy of flurbiprofen axetil being a prodrug of a nonsteroidal anti-inflammatory drug for preventing postanesthetic shivering in patients undergoing gynecologic laparotomy surgeries. Method This study is a retrospective observational study. I collected data from patients undergoing gynecologic laparotomy surgeries performed between October 1, 2019, and September 30, 2020, at Kumamoto City Hospital. All the patients were managed with general anesthesia with or without epidural analgesia. The administration of intravenous 50 mg flurbiprofen axetil for postoperative pain control at the end of the surgery was left to the individual anesthesiologist. The patients were divided into two groups: those who had received intravenous flurbiprofen axetil (flurbiprofen group) and those who had not received intravenous flurbiprofen axetil (non-flurbiprofen group), and I compared the frequency of postanesthetic shivering between the two groups. Additionally, the factors presumably associated with postanesthetic shivering were collected from the medical charts. Intergroup differences were assessed with the χ2 test with Yates’ correlation for continuity category variables. The Student’s t test was used to test for differences in continuous variables. Furthermore, a multivariate logistic regression analysis was performed to elucidate the relationship between the administration of flurbiprofen axetil and the incidence of PAS. Results I retrospectively examined the cases of 141 patients aged 49 ± 13 (range 21-84) years old. The overall postanesthetic shivering rate was 21.3% (30 of the 141 patients). The frequency of postanesthetic shivering in the flurbiprofen group (n = 31) was 6.5%, which was significantly lower than that in the non-flurbiprofen group (n = 110), 25.5% (p value = 0.022). A multivariate logistic regression analysis showed that administration of flurbiprofen axetil was independently associated with a reduced incidence of postanesthetic shivering (odds ratio 0.12; 95% confidence interval, 0.02-0.66, p value = 0.015). Conclusions My result suggests that intraoperative 50 mg flurbiprofen axetil administration for postoperative pain control is useful to prevent postanesthetic shivering in patients undergoing gynecologic laparotomy surgeries.

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