• Health Policy and Management
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• v.18 no.4
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• pp.85-110
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• 2008

In this study, we explore the long-term demand forecasting of high-price medical equipments based on logistic and Bass diffusion model. We analyze the specific pattern of each equipment's diffusion curve by interpreting the parameter estimates of Bass diffusion model. Our findings are as follows. First, ultrasonic imaging system, CT are in the stage of maturity and so, the future demands of them are not too large. Second, medical image processing unit is between growth stage and maturity stage and so, the demand is expected to increase considerably for two or three years. Third, MRI is in the stage of take-off and Mammmography X-ray system is in the stage of maturity but, estimates of the potential number of adopters based on logistic model is considerably different to that based on Bass diffusion model. It means that additional data for these two equipments should be collected and analyzed to obtain the reliable estimates of their demands. Fourth, medical image processing unit have the largest q value. It means that the word-of-mouth effect is important in the diffusion of this equipment. Fifth, for MRI and Ultrasonic system, q/p values have the relatively large value. It means that collective power has an important role in adopting these two equipments.

• Asian Oncology Nursing
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• v.5 no.1
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• pp.3-10
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• 2005

Back ground: Nurses now occupy one third of all hospital human resources. Therefore, efficient management of nursing manpower is getting more important. While it is very clear that nursing workload requirement analysis and patient severity classification should be done first for the efficient allocation of nursing workforce, these processes have been conducted manually with ad hoc rule. Purposes: This study was tried to make a predict model for patient classification according to nursing need. We tried to find the easier and faster method to classify nursing patients that can help efficient management of nursing manpower. Methods: The nursing patient classifications data of the hospitalized cancer patients in one of the biggest cancer center in Korea during 2003.1.1-2003.12.31 were assessed by trained nurses. This study developed a prediction model and analyzing nursing needs by data mining techniques. Patients were classified by three different data mining techniques, (Logistic regression, Decision tree and Neural network) and the results were assessed. Results: The data set was created using 165,073 records of 2,228 patients classification database. Main explaining variables were as follows in 3 different data mining techniques. 1) Logistic regression : age, month and section. 2) Decision tree : section, month, age and tumor. 3) Neural network : section, diagnosis, age, sex, metastasis, hospital days and month. Among these three techniques, neural network showed the best prediction power in ROC curve verification. As the result of the patient classification prediction model developed by neural network based on nurse needs, the prediction accuracy was 84.06%. Conclusion: The patient classification prediction model was developed and tested in this study using real patients data. The result can be employed for more accurate calculation of required nursing staff and effective use of labor force.

• Research in Community and Public Health Nursing
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• v.33 no.3
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• pp.299-311
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• 2022

Purpose: Untreated depression in adolescents affects their entire life. It is important to detect and intervene early depression in adolescence considering the characteristics of adolescent's depressive symptoms accompanied by internalization and externalization. The aim of this study was to identify latent classes of depressive symptom trajectories of adolescents and determinants of classes in Korea. Methods: The three time-point (2018~2020) data derived from the Korean Children and Youth Panel Survey 2018 were used (N=2,325). Latent Growth Curve Modeling (LGCM) was conducted to explore the depressive symptom trajectories in all adolescents, and Latent Class Growth Modeling (LCGM) was conducted to identify each latent class. Multinomial logistic regression analysis was performed to confirm the determinants of each latent class. Results: The LGCM results showed that there was no statistically significant change in all adolescents' depressive symptoms for 3 years. However, the LCGM results showed that four latent classes showing different trajectories were distinguished: 1) Low-stable (intercept=14.39, non-significant slope), 2) moderate-increasing (intercept=19.62, significantly increasing slope), 3) high-stable (intercept=26.30, non-significant slope), and 4) high-rapidly decreasing (intercept=26.34, significantly rapidly decreasing slope). The multinomial logistic regression analysis showed that the significant determinants (i.e., gender, self-esteem, aggression, somatization, peer relationship) of each latent class were different. Conclusion: When screening adolescent's depression, it is necessary to monitor not only direct depression symptoms but also self-esteem, aggression, somatization symptoms, and peer relationships. The findings of this study may be valuable for nurses and policy makers to develop mental health programs for adolescents.

• Journal of Korean Society of Rural Planning
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• v.16 no.2
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• pp.47-55
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• 2010

Land cover and land use change data are important in many studies including climate change and hydrological studies. Although the various theories and models have been developed, it is difficult to identify the driving factors of the land use change because land use change is related to policy options and natural and socio-economic conditions. This study is to attempt to simulate the land cover change using the CLUE model based on a statistical analysis of land-use change. CLUE model has dynamic modeling tools from the competition among land use change in between driving force and land use, so that this model depends on statistical relations between land use change and driving factors. In this study, Yongin, Icheon and Anseong were selected for the study areas, and binary logistic regression and factor analysis were performed verifying with ROC curve. Land cover probability map was also prepared to compare with the land cover data and higher probability areas are well matched with the present land cover demonstrating CLUE model applicability.

• Journal of Yeungnam Medical Science
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• v.38 no.3
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• pp.225-230
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• 2021

Background: We aimed to analyze the effectiveness of albumin to globulin ratio (AGR) in predicting postoperative febrile urinary tract infection (fUTI) after ureteroscopic lithotripsy (URS) and retrograde intrarenal surgery (RIRS). Methods: From January 2013 to May 2018, 332 patients underwent URS and RIRS. The rate of postoperative fUTI and risk factors for postoperative fUTI were analyzed using logistic regression. Patients were divided into postoperative fUTI and non-postoperative fUTI (non-fUTI) groups. AGR with other demographic and perioperative data were compared between the two groups to predict the development of fUTI after URS. Results: Of the 332 patients, postoperative fUTI occurred in 41 (12.3%). Preoperative pyuria, microscopic hematuria, diabetes mellitus, hypoalbuminemia, and hyperglobulinemia were more prevalent in the fUTI group. Patients in the fUTI group had larger stone size, lower preoperative AGR, longer operation time, and longer preoperative antibiotic coverage period. In a multivariable logistic analysis, preoperative pyuria, AGR, and stone size were independently correlated with postoperative fUTI (p<0.001, p=0.008, and p=0.041, respectively). Receiver operating curve analysis showed that the cutoff value of AGR that could predict a high risk of fUTI after URS was 1.437 (sensitivity, 77.3%; specificity, 76.9%), while the cutoff value of stone size was 8.5 mm (sensitivity, 55.3%; specificity, 44.7%). Conclusion: This study demonstrated that preoperative pyuria, AGR, and stone size can serve as prognostic factors for predicting fUTI after URS.

• Investigative Magnetic Resonance Imaging
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• v.25 no.2
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• pp.118-128
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• 2021

Purpose: To investigate the value of MR textural analysis, including use of diffusion-weighted imaging (DWI) to differentiate malignant from benign soft-tissue tumors on 3T MRI. Materials and Methods: We enrolled 69 patients (25 men, 44 women, ages 18 to 84 years) with pathologically confirmed soft-tissue tumors (29 benign, 40 malignant) who underwent pre-treatment 3T-MRI. We calculated MR texture, including mean, standard deviation (SD), skewness, kurtosis, mean of positive pixels (MPP), and entropy, according to different spatial-scale factors (SSF, 0, 2, 4, 6) on axial T1- and T2-weighted images (T1WI, T2WI), contrast-enhanced T1WI (CE-T1WI), high b-value DWI (800 sec/mm²), and apparent diffusion coefficient (ADC) map. We used the Mann-Whitney U test, logistic regression, and area under the receiver operating characteristic curve (AUC) for statistical analysis. Results: Malignant soft-tissue tumors had significantly lower mean values of DWI, ADC, T2WI and CE-T1WI, MPP of ADC, and CE-T1WI, but significantly higher kurtosis of DWI, T1WI, and CE-T1WI, and entropy of DWI, ADC, and T2WI than did benign tumors (P < 0.050). In multivariate logistic regression, the mean ADC value (SSF, 6) and kurtosis of CE-T1WI (SSF, 4) were independently associated with malignancy (P ≤ 0.009). A multivariate model of MR features worked well for diagnosis of malignant soft-tissue tumors (AUC, 0.909). Conclusion: Accurate diagnosis could be obtained using MR textural analysis with DWI and CE-T1WI in differentiating benign from malignant soft-tissue tumors.

• Research in Community and Public Health Nursing
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• v.30 no.4
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• pp.449-459
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• 2019

Purpose: The aim of this study is to explore different types of self-rated health trajectories among one-person households in Korea. Methods: We used five time-point data derived from Korea Health Panel (2011~2015). A latent growth curve modeling was used to assess the overall feature of self-rated health trajectory in one-person households, and a latent class growth modeling was used to determine the number and shape of trajectories. We then applied multinomial logistic regression on each class to explore the predicting variables. Results: We found that the overall slope of self-rated health in one-person households decreases. In addition, latent class analysis demonstrated three classes: 1) High-Decreasing class (i.e., high intercept, significantly decreasing slope), 2) Moderate-Decreasing class (i.e., average intercept, significantly decreasing slope), and 3) Low-Stable class (i.e., low intercept, flat and nonsignificant slope). The multinomial logistic regression analysis showed that the predictors of each class were different. Especially, one-person households with poor health condition early were at greater risk of being Low-Stable class compared with High-Decreasing class group. Conclusion: The findings of this study demonstrate that more attentions to one-person households are needed to promote their health status. Policymakers may develop different health and welfare programs depending on different characteristics of one-person household trajectory groups in Korea.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.3
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• pp.927-930
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• 2012

Background and Objectives: Artificial neural networks (ANNs) are flexible and nonlinear models which can be used by clinical oncologists in medical research as decision making tools. This study aimed to predict distant metastasis (DM) of colorectal cancer (CRC) patients using an ANN model. Methods: The data of this study were gathered from 1219 registered CRC patients at the Research Center for Gastroenterology and Liver Disease of Shahid Beheshti University of Medical Sciences, Tehran, Iran (January 2002 and October 2007). For prediction of DM in CRC patients, neural network (NN) and logistic regression (LR) models were used. Then, the concordance index (C index) and the area under receiver operating characteristic curve (AUROC) were used for comparison of neural network and logistic regression models. Data analysis was performed with R 2.14.1 software. Results: The C indices of ANN and LR models for colon cancer data were calculated to be 0.812 and 0.779, respectively. Based on testing dataset, the AUROC for ANN and LR models were 0.82 and 0.77, respectively. This means that the accuracy of ANN prediction was better than for LR prediction. Conclusion: The ANN model is a suitable method for predicting DM and in that case is suggested as a good classifier that usefulness to treatment goals.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.4199-4202
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• 2012

Objective: The aim of our study is to compare an ovarian malignancy prediction model based on age and four sonographic findings (OMPS1) with a new model called OMPS2 which differs just by adding serum CA125 measurement to (OMPS1). Methods: In a cross sectional comparative study OMPS1 was validated in 830 operated ovarian masses within a 3 years period (2006-2009). Logistic regression analysis was used to construct OMPS2 based on OMPS1 adding serum CA125 findings. The area under the curve for two models was compared in 411 patients. Results: OMPS2 was calculated as follows: OMPS1 + 1.444 (if serum CA125= 36-200) or 3.842 (if serum CA125 is more than 200). AUC of OMPS2 was increased to 84.3% (CI 95% 78.1- 89.8) in comparison to OMPS1 with AUC of 78.1% (CI 95% 71.8-84.5). Conclusion: Our second model is more accurate in prediction of ovarian malignancy, compared with our first model.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.4
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• pp.1861-1864
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• 2016

Although early diagnosis of thyroid nodule type is very important, the diagnostic accuracy of standard tests is a challenging issue. We here aimed to find an optimal combination of factors to improve diagnostic accuracy for distinguishing malignant from benign thyroid nodules before surgery. In a prospective study from 2008 to 2012, 345 patients referred for thyroidectomy were enrolled. The sample size was split into a training set and testing set as a ratio of 7:3. The former was used for estimation and variable selection and obtaining a linear combination of factors. We utilized smoothly clipped absolute deviation (SCAD) logistic regression to achieve the sparse optimal combination of factors. To evaluate the performance of the estimated model in the testing set, a receiver operating characteristic (ROC) curve was utilized. The mean age of the examined patients (66 male and 279 female) was $40.9{\pm}13.4years$ (range 15- 90 years). Some 54.8% of the patients (24.3% male and 75.7% female) had benign and 45.2% (14% male and 86% female) malignant thyroid nodules. In addition to maximum diameters of nodules and lobes, their volumes were considered as related factors for malignancy prediction (a total of 16 factors). However, the SCAD method estimated the coefficients of 8 factors to be zero and eliminated them from the model. Hence a sparse model which combined the effects of 8 factors to distinguish malignant from benign thyroid nodules was generated. An optimal cut off point of the ROC curve for our estimated model was obtained (p=0.44) and the area under the curve (AUC) was equal to 77% (95% CI: 68%-85%). Sensitivity, specificity, positive predictive value and negative predictive values for this model were 70%, 72%, 71% and 76%, respectively. An increase of 10 percent and a greater accuracy rate in early diagnosis of thyroid nodule type by statistical methods (SCAD and ANN methods) compared with the results of FNA testing revealed that the statistical modeling methods are helpful in disease diagnosis. In addition, the factor ranking offered by these methods is valuable in the clinical context.