• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.329-342
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• 2018

Nomogram is a statistical tool that visualizes the risk factors of the disease and then helps to understand the untrained people. This study used risk factors of chronic obstructive pulmonary disease (COPD) and compared with logistic regression model and naïve Bayesian classifier model. Data were analyzed using the Korean National Health and Nutrition Examination Survey 6th (2013-2015). First, we used 6 risk factors about COPD. We constructed nomogram using logistic regression model and naïve Bayesian classifier model. We also compared the nomograms constructed using the two methods to find out which method is more appropriate. The receiver operating characteristic curve and the calibration plot were used to verify each nomograms.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.573-585
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• 2018

In this study, we fit the logistic regression model and naïve Bayesian classifier model using 11 risk factors to predict the incidence rate probability for type 2 diabetes mellitus. We then introduce how to construct a nomogram that can help people visually understand it. We use data from the 2013-2015 Korean National Health and Nutrition Examination Survey (KNHANES). We take 3 interactions in the logistic regression model to improve the quality of the analysis and facilitate the application of the left-aligned method to the Bayesian nomogram. Finally, we compare the two nomograms and examine their utility. Then we verify the nomogram using the ROC curve.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.591-602
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• 2017

The concentration of fine dust has increased in Korea and people have become more concerned with respiratory diseases. This study selected risk factors for chronic obstructive pulmonary disease (COPD) through demographic and clinical features and constructed a nomogram. First, logistic regression analysis was performed using demographic and clinical feature and the pulmonary function test results of the Korean National Health and Nutrition Examination Survey (KNHANES) $6^{th}$ (2013-2015) and the nomogram was constructed to visualize the risk factors of chronic obstructive pulmonary disease in order to facilitate the interpretation of the analysis results. The ROC curve and calibration plot were also used to verify the nomogram of chronic obstructive pulmonary disease.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.796-801
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• 2009

In medical practice, the diagnosis or prediction models requiring complicated computations are not widely recognized due to difficulty in interpreting the course of reasoning and the complexity of computations. Medical personnel have used the nomograms which are a graphical representation for numerical relationships that enables to easily compute a complicated function without help of computation machines. It has been widely paid attention in diagnosing diseases or predicting the progress of diseases. A nomogram is constructed from a set of clinical data which contain various attributes such as symptoms, lab experiment results, therapy history, progress of diseases or identification of diseases. It is of importance to select effective ones from available attributes, sometimes along with parameters accompanying the attributes. This paper introduces a nomogram construction method that uses a naive Bayesian technique to construct a nomogram as well as a genetic algorithm to select effective attributes and parameters. The proposed method has been applied to the construction of a nomogram for a real clinical data set.

• Obstetrics & gynecology science
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• v.61 no.6
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• pp.669-674
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• 2018

Objective This study aimed to develop a nomogram that predicts ongoing pregnancy after in vitro fertilization and embryo transfer (IVF-ET) using patient age and serum hormonal markers. Methods A total of 284 IVF-ET cycles were retrospectively analyzed. At 14 days post-oocyte pick-up (OPU), the serum human chorionic gonadotropin (HCG) and progesterone levels were measured. The main predicted outcome was ongoing pregnancy. Results Patient age and serum of HCG and progesterone levels at 14 days post-OPU were good predictors of ongoing pregnancy. The cut-off value and area under the curve (AUC) (95% confidence interval) were 36.5 years and 0.666 (0.599-0.733), respectively, for patient age; 67.8 mIU/mL and 0.969 (0.951-0.987), respectively, for serum HCG level; and 29.8 ng/mL and 0.883 (0.840-0.925), respectively, for serum progesterone level. When the prediction model was constructed using these three parameters, the addition of serum progesterone level to the prediction model did not increase its overall predictability. Furthermore, a high linear co-relationship was found between serum HCG and progesterone levels. Therefore, we developed a new nomogram using patient age and HCG serum level only. The AUC of the newly developed nomogram for predicting ongoing pregnancy after IVF-ET cycles using patient age and serum HCG level was as high as 0.975. Conclusion We showed that ongoing pregnancy may be predicted using only patient age and HCG serum level. Our nomogram could help clinicians and patients predict ongoing pregnancy after IVF-ET if the serum JCG level was ${\geq}5IU/L$ at 14 days post-OPU.

• YAKHAK HOEJI
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• v.53 no.2
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• pp.83-88
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• 2009

Warfarin is the most widely used oral anticoagulant in the world but maintenance of proper therapeutic range and prevention of adverse drug events always need to be careful. Especially, in Korea, warfarin dosing for patients with cerebral infarction is currently based on the nomogram which is done by foreign clinical trials not for the Korean. Therefore we evaluate warfarin dose of patients in the neurology and eventually get the base data of warfarin nomogram for Korean with stroke. We performed this study retrospectively on reviewing the medical charts to evaluate the prescribed loading dose (LD) and maintenance dose (MD) of warfarin and each responding International Normalized Ratio (INR) with any bleeding adverse drug reaction including of patient's characteristics for total 75 patients with stroke in the department of neurology of Kangnam ST. Mary's Hospital from January 2005 to June 2008. All evaluated patients should not be treated with warfarin in the past at all and should be initiated warfarin therapy first.ly at this time. All evaluated patients were divided as two classes by wafarin LD which is; 1) HDG - a high loading dosing group prescribed over 5mg, and 2) LDG - a low loading dosing group prescribed 5mg or below. As a result, average LD was $9.34{\pm}0.22$ mg (p=0.000) in HDG and $4.25{\pm}0.39$ mg (p=0.000) in LDG. Average baseline INR was $0.91{\pm}0.05$ (p=0.161) in HDG and $1.26{\pm}0.14$ (p=0.002) in LDG. On the first and second week, daily MD was $4.21{\pm}0.14$ mg (p=0.000) and $2.96{\pm}0.19$ mg (p=0.696) in HDG and also in LDG, $2.95{\pm}0.29$ mg (p=0.000) and $3.14{\pm}0.36$ mg (p=0.696). Also average reacting daily INR was respectively $2.53{\pm}0.12$ (p=0.141) and $2.51{\pm}0.16$ (p=0.678) in HDG, and in LDG, $2.11{\pm}0.17$ (p=0.141) and $2.42{\pm}0.14$ (p=0.678). After the second week, INR was not measured in regularly. Also most of underlying diseases were hypertension (n=38), diabetes mellitus (n=14), dyslipidemia (n=8) in order. Four ADRs with simple hemorrhage were occurred and those were due to drug interaction by comedication. In the conclusion, proper starting LD for Korean with stroke is 10 mg if baseline INR is around 1.0 or 5 mg if over 1.3. Proper MD need to be more evaluated in the future for setting up warfarin nomogram to make prospective study.

• Korean Architects
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• no.6 s.44
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• pp.10-14
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• 1972

• Korean Architects
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• no.8 s.46
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• pp.37-40
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• 1972

• Korean Architects
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• no.10 s.48
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• pp.42-45
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• 1972

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.12
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• pp.6181-6185
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• 2012

Background: The aim of the study was to evaluate the available breast nomograms (MSKCC, Stanford, Tenon) to predict non-sentinel lymph node metastasis (NSLNM) and to determine variables for NSLNM in SLN positive breast cancer patients in our population. Materials and Methods: We retrospectively reviewed 170 patients who underwent completion axillary lymph node dissection between Jul 2008 and Aug 2010 in our hospital. We validated three nomograms (MSKCC, Stanford, Tenon). The likelihood of having positive NSLNM based on various factors was evaluated by use of univariate analysis. Stepwise multivariate analysis was applied to estimate a predictive model for NSLNM. Four factors were found to contribute significantly to the logistic regression model, allowing design of a new formula to predict non-sentinel lymph node metastasis. The AUCs of the ROCs were used to describe the performance of the diagnostic value of MSKCC, Stanford, Tenon nomograms and our new nomogram. Results: After stepwise multiple logistic regression analysis, multifocality, proportion of positive SLN to total SLN, LVI, SLN extracapsular extention were found to be statistically significant. AUC results were MSKCC: 0.713/Tenon: 0.671/Stanford: 0.534/DEU: 0.814. Conclusions: The MSKCC nomogram proved to be a good discriminator of NSLN metastasis in SLN positive BC patients for our population. Stanford and Tenon nomograms were not as predictive of NSLN metastasis. Our newly created formula was the best prediction tool for discriminate of NSLN metastasis in SLN positive BC patients for our population. We recommend that nomograms be validated before use in specific populations, and more than one validated nomogram may be used together while consulting patients.