• The Korean Journal of Health Service Management
• /
• v.9 no.2
• /
• pp.81-88
• /
• 2015

The purpose of this study was to compare the risk-adjusted in-hospital mortality for craniotomies between logistic regression and multilevel analysis. By using patient sample data from the Health Insurance Review & Assessment Service, in-patients with a craniotomy were selected as the survey target. The sample data were collected from a total number of 2,335 patients from 90 hospitals. The sample data were analyzed with SAS 9.3. From the results of the existing logistic regression analysis and multilevel analysis, the values from the multilevel analysis represented a better model than that of logistic regression. The intra-class correlation (ICC) was 18.0%. It was found that risk-adjusted in-hospital mortality for craniotomies may vary in every hospital. The agreement by kappa coefficient between the two methods was good for the risk-adjusted in-hospital mortality for craniotomies, but the factors influencing the outcome for that were different.

• Health Policy and Management
• /
• v.10 no.2
• /
• pp.1-21
• /
• 2000

This study reviewde various parametic and nonparametic method for forexasting hospital closures in Korea. We compared multivariate discriminant analysis, multivartiate logistic regression, classfication and regression tree, and neural network method based on hit ratio of each model for forecasting hospital closure. Like other studies in the literture, neural metwork analysis showed highest average hit ratio. For policy and business purposes, we combined the four analytical method and constructed a foreasting model that can be easily used to predict the probabolity of hospital closure given financial information of a hospital.

• Korean Journal of Biological Psychiatry
• /
• v.12 no.2
• /
• pp.165-172
• /
• 2005

Objectives:The purpose of this study is to 1) conduct a discrimination analysis of schizophrenia and bipolar affective disorder using MMPI profile through artificial neural network analysis and logistic regression analysis, 2) to make a comparison between advantages and disadvantages of the two methods, and 3) to demonstrate the usefulness of artificial neural network analysis of psychiatric data. Procedure:The MMPI profiles for 181 schizophrenia and bipolar affective disorder patients were selected. Of these profiles, 50 were randomly placed in the learning group and the remaining 131 were placed in the validation group. The artificial neural network was trained using the profiles of the learning group and the 131 profiles of the validation group were analyzed. A logistic regression analysis was then conducted in a similar manner. The results of the two analyses were compared and contrasted using sensitivity, specificity, ROC curves, and kappa index. Results:Logistic regression analysis and artificial neural network analysis both exhibited satisfactory discriminating ability at Kappa index of greater than 0.4. The comparison of the two methods revealed artificial neural network analysis is superior to logistic regression analysis in its discriminating capacity, displaying higher values of Kappa index, specificity, and AUC(Area Under the Curve) of ROC curve than those of logistic regression analysis. Conclusion:Artificial neural network analysis is a new tool whose frequency of use has been increasing for its superiority in nonlinear applications. However, it does possess insufficiencies such as difficulties in understanding the relationship between dependent and independent variables. Nevertheless, when used in conjunction with other analysis tools which supplement it, such as the logistic regression analysis, it may serve as a powerful tool for psychiatric data analysis.

• Journal of the Korea Safety Management & Science
• /
• v.10 no.3
• /
• pp.55-62
• /
• 2008

This is to prevent accidents that can be caused during the process of hospital logistics and accidents in relation to the traceability of medical wastes. And this is also to set up the logistic management system of medical wastes is hospital where the safety of patients shall to regard as the first priority. through these case studies effective operating plans shall be provided.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
• /
• v.18 no.1
• /
• pp.19-29
• /
• 2012

Background: This study was conducted to investigate subjective musculoskeletal symptom and the related factor of caregiver. Methods: For 300 caregiver, we used the self-administered questionnaires to examine occupational stress and subjective musculoskeletal symptom designed by KOSHA. The collected data were analyzed chi-square test, independent t-test and multiple logistic regression analysis using SPSS 12.0. Results: The multiple logistic regression analysis showed that the caregiver working in the general hospital significantly increased the subjective musculoskeletal symptom in their neck, shoulder, hand/wrist/finger, back, leg/foot. For the caregiver working in hospital showed significantly increased the subjective musculoskeletal symptom in their hand/wrist/finger and leg/foot. Conclusions: With the above results, continuous and systematic prevention program should be established, which include the ergonomics and psychosocial factor for the caregiver's musculoskeletal symptom.

• Korean Journal of Psychosomatic Medicine
• /
• v.25 no.1
• /
• pp.56-62
• /
• 2017

Objectives : The objective of this study was to investigate the relationship between psychological distress and pain in cancer patients. Methods : 249 patients with cancer who visited National Health Insurance Service Ilsan Hospital between April 2013 and March 2014 were evaluated with National Cancer Center Psychological Symptom Inventory(NCC-PSI) which consisted of Modified Distress Thermometer(MDT) and Modified Impact Thermometer(MIT). Each scale was divided into 3 subscales targeting separate symptoms: insomnia, anxiety, and depression. Psychological distress was defined as positive for those who scored above the cutoff values in at least one of all six subscales. The Numeric Rating Scale for Pain(NRS-Pain) was used to assess the subjective severity of pain. Logistic regression was performed to investigate the association between psychological distress and pain. Results : Univariate logistic regression analysis showed that pain, gender, compliance, and two subscale scores of Hospital Anxiety and Depression Scale(HADS) were significantly associated with psychological distress. Multivariate logistic regression analysis showed that pain and HADS anxiety subscale score maintained a statistically significant association with psychological distress adjusted for variables including age, gender, years of education, Eastern Cooperative Oncology Group performance status, cancer stage, Charlson Comorbidity Index, compliance, and HADS depression subscale score. One point increase in pain was 1.31 times more likely to cause psychological distress. In secondary analysis, pain was significantly associated with all subscales of NCC-PSI, except MIT-anxiety subscale. Conclusions : This study suggests that NCC-PSI, a screening tool for psychological distress, reflects pain. We recommend that physicians who treat cancer patients consider the examination of psychological distress which provides comprehensive evaluation of various factors regarding quality of life.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.7
• /
• pp.53-59
• /
• 2016

The purpose of this study was to investigate the factors of hospital arrival delays of acute ischemic stroke patients. The study subjects were 126 cerebral infarction patients G Metropolitan City university hospital emergency center. General characteristics, disease-related characteristics and stroke-related were collected by self-reported questionnaires. Hospital arrival times by subjects' characteristics were tested by $x^2$ test and logistic regression analysis. Of 126 cerebral infarction patients, Their average hours taken to move to a hospital was 12.7 hours with the fastest case being 0.5 hour and the most delayed case being 127.8 hours. 61.1%(77 persons) of the stroke patients under this experiment said to have taken 3 hours or less. In logistic regression analyses, Coming to the hospital directly without passing through other hospitals was found to have higher probability of arriving less than 3 hours(${\beta}$=2.960, p=.009), And if LAPSS was tested positive, such cases are more likely to arrive within 3 hours(${\beta}$=2.219, p=.049). For acute ischemic stroke and caregivers need training to be conducted promptly admitted to hospitals for education and treatment hospital stroke screening will help to improve the treatment of stroke patients

• Journal of Korean Neurosurgical Society
• /
• v.63 no.5
• /
• pp.640-648
• /
• 2020

Objective : This study aimed to assess the relationship between increased intracranial pressure (ICP) and mastoid effusions (ME). Methods : Between January 2015 and October 2018, patients who underwent intracranial surgery and had ICP monitoring catheters placed were enrolled. ICP was recorded hourly for at least 3 days. ME was determined by the emergence of opacification in mastoid air cells on follow-up brain imaging. C-reactive protein (CRP) levels, presence of endotracheal tube (ETT) and nasogastric tube (NGT), duration of intensive care unit (ICU) stay, duration of mechanical ventilator application, diagnosis, surgical modalities, and presence of sinusitis were recorded. Each factor's effect on the occurrence of ME was analyzed by binary logistic regression analyses. To analyze the independent effects of ICP as a predictor of ME a multivariable logistic regression analysis was performed. Results : Total of 61 (53%) out of 115 patients had ME. Among the patients who had unilateral brain lesions, 94% of subject (43/50) revealed the ipsilateral development of ME. ME developed at a mean of 11.1±6.2 days. The variables including mean ICP, peak ICP, age, trauma, CRP, ICU stays, application of mechanical ventilators and presence of ETT and NGT showed statistically significant difference between ME groups and non-ME groups in univariate analysis. Sex and the occurrence of sinusitis did not differ between two groups. Adding the ICP variables significantly improved the prediction of ME in multivariable logistic regression analysis. Conclusion : While multiple factors affect ME, this study demonstrates that ICP and ME are probably related. Further studies are needed to determine the mechanistic relationship between ICP and middle ear pressure.

• Health Policy and Management
• /
• v.13 no.3
• /
• pp.21-34
• /
• 2003

Patient revisit to used hospital is a key factor in determining a health care organization's competitive advantage and survival. This article examines the relationship between customer's satisfaction and his/her revisit associated with three different methods which are the Chi Square Automatic Interaction Detection(CHAID) for segmenting the outpatient group, logistic regression and neural networks for addressing the outpatient's revisit. The main findings indicate that the important factors on outpatient's revisit are physician's kindness, nurse's skill, overall level of satisfaction, hospital reputation, recommendation, level of diagnoses and outpatient's age. Among these ones, physician's kindness is the most important factor as guidelines for decision of their revisit. The decision maker of hospital should select the strategy containing the variable amount of the level of revisit and size of outpatient's group under the constraint on the hospital's time, budget and manpower given. Finally, this study shows that neural networks, as non-parametric technique, appear to more correctly predict revisit than does logistic regression as a parametric estimation technique.

• Korean Journal of Radiology
• /
• v.25 no.1
• /
• pp.43-54
• /
• 2024

Objective: To evaluate the added value of diffusion-weighted imaging (DWI)-based quantitative parameters to distinguish uterine sarcomas from atypical leiomyomas on preoperative magnetic resonance imaging (MRI). Materials and Methods: A total of 138 patients (age, 43.7 ± 10.3 years) with uterine sarcoma (n = 44) and atypical leiomyoma (n = 94) were retrospectively collected from four institutions. The cohort was randomly divided into training (84/138, 60.0%) and validation (54/138, 40.0%) sets. Two independent readers evaluated six qualitative MRI features and two DWI-based quantitative parameters for each index tumor. Multivariable logistic regression was used to identify the relevant qualitative MRI features. Diagnostic classifiers based on qualitative MRI features alone and in combination with DWI-based quantitative parameters were developed using a logistic regression algorithm. The diagnostic performance of the classifiers was evaluated using a cross-table analysis and calculation of the area under the receiver operating characteristic curve (AUC). Results: Mean apparent diffusion coefficient value of uterine sarcoma was lower than that of atypical leiomyoma (mean ± standard deviation, 0.94 ± 0.30 10^-3 mm²/s vs. 1.23 ± 0.25 10^-3 mm²/s; P < 0.001), and the relative contrast ratio was higher in the uterine sarcoma (8.16 ± 2.94 vs. 4.19 ± 2.66; P < 0.001). Selected qualitative MRI features included ill-defined margin (adjusted odds ratio [aOR], 17.9; 95% confidence interval [CI], 1.41-503, P = 0.040), intratumoral hemorrhage (aOR, 27.3; 95% CI, 3.74-596, P = 0.006), and absence of T2 dark area (aOR, 83.5; 95% CI, 12.4-1916, P < 0.001). The classifier that combined qualitative MRI features and DWI-based quantitative parameters showed significantly better performance than without DWI-based parameters in the validation set (AUC, 0.92 vs. 0.78; P < 0.001). Conclusion: The addition of DWI-based quantitative parameters to qualitative MRI features improved the diagnostic performance of the logistic regression classifier in differentiating uterine sarcomas from atypical leiomyomas on preoperative MRI.