• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.28-37
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• 2011

With growing concerns about healthy life recently, although the podiatry which deals with the whole area for diagnosis, treatment of foot and leg, and prevention has been widely interested, research in our country is not active. Also, because most of the previous researches in data analysis performed the quantitative approaches, the reasonable level of reliability for clinical application could not be guaranteed. Clinical data mining utilizes various data mining analysis methods for clinical data, which provides decision support for expert's diagnosis and treatment for the patients. Because the decision tree can provide good explanation and description for the analysis procedure and is easy to interpret the results, it is simple to apply for clinical problems. This study investigate rules of item of diagnosis in disease types for adapting decision tree after collecting diagnosed data patients who are 2620 feet of 1310(males:633, females:677) in shoes clinic (department of rehabilitation medicine, Chungnam National University Hospital). and we classified 15 foot diseases followed factor of 22 foot diseases, which investigated diagnosis of 64 rules. Also, we analyzed and compared correlation relationship of characteristic of disease and factor in types through made decision tree from 5 class types(infants, child, adolescent, adult, total). Investigated results can be used qualitative and useful knowledge for clinical expert`s, also can be used tool for taking effective and accurate diagnosis.

• Asia pacific journal of information systems
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• v.16 no.2
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• pp.69-84
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• 2006

Importance of Today's diffusion of integrated hospital information systems is that various and huge amount of data is being accumulated in their database systems. Many researchers have studied utilizing such hospital data. While most researches were conducted mainly for medical diagnosis, there have been insufficient studies to develop medical care cost prediction model, especially using machine learning techniques. In this research, therefore, we built a medical care cost prediction model for cancer patients using CBR (Case-Based Reasoning), one of the machine learning techniques. Its performance was compared with those of Neural Networks and Decision Tree models. As a result of the experiment, the CBR prediction model was shown to be the best in general with respect to error rate and linearity between real values and predicted values. It is believed that the medical care cost prediction model can be utilized for the effective management of limited resources in hospitals.

• Journal of the Korean Society of Radiology
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• v.83 no.3
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• pp.737-743
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• 2022

Aspergillus tracheobronchitis, an uncommon form of invasive pulmonary aspergillosis, is characterized by the development of a pseudomembrane, ulcers, or an obstruction that is predominantly confined to the tracheobronchial tree. Pseudomembranous Aspergillus tracheobronchitis is the most severe form of Aspergillus tracheobronchitis, and only a few cases have been reported in Korea. We report the characteristic chest CT findings in a patient diagnosed with pseudomembranous Aspergillus tracheobronchitis after bronchoscopy and successfully treated by proper antifungal treatment.

• Journal of Korean Neurosurgical Society
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• v.67 no.1
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• pp.94-102
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• 2024

Objective : The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML). Methods : Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR). Results : We identified 71 patients who had favorable outcomes and 156 who had unfavorable outcomes. The results showed that the SVM model achieved the best comprehensive prediction efficiency. For the SVM model, the AUC, accuracy, specificity, sensitivity, PLR, NLR, and DOR were 0.91, 0.92, 0.92, 0.93, 11.63, 0.076, and 153.03, respectively. For the SVM model, we found the importance value of time to operating room (TOR) was higher significantly than other variables. Conclusion : The analysis of clinical reliability showed that the SVM model achieved the best comprehensive prediction efficiency and the importance value of TOR was higher significantly than other variables.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.401-409
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• 2020

The purpose of this study is to develop a convergence inpatient medical service patient experience management model(IMSPEMM) that can help in the management strategy of a medical institution to create a patient-centered medical culture. Using the original data from the 2018 Medical Service Experience Survey, 593 people with medical services inpatient(MSI) over the age of 15 were analyzed. By using the decision tree model, we developed a prediction model for overall satisfaction(OS) with the inpatient medical service experience(IMSE) and the intention to recommend patient experience(RI), and were classified into 4 and 7 types. The accuracy of the model was 68.9% and 78.3%. The OS level of IMSE was the nurse area and the hospital room noise management area, and the RI decision factor was the nurse area. It is significant that the IMSPEMM for MSI was presented and confirmed that the nurse area and the noise management area of the hospital room are important factors for the inpatient experience. It is considered that further research is needed to generalize the IMSPEMM.

• Journal of Microbiology and Biotechnology
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• v.30 no.6
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• pp.885-892
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• 2020

Chemotherapy regimens for non-small cell lung cancer (NSCLC) have various adverse effects on the human body. For this reason, probiotics have received attention regarding their potential value as a safe and natural complementary strategy for cancer prevention. This study analyzed the anticancer effects of aqueous extracts of probiotic bacteria Bifidobacterium bifidum (BB), Bifidobacterium longum (BL), Bifidobacterium lactis (BLA), Bifidobacterium infantis 1 (BI1), and Bifidobacterium infantis 2 (BI2) on NSCLC cell lines. When the aqueous extracts of probiotic Bifidobacterium species were applied to the NSCLC cell lines A549, H1299, and HCC827, cell death increased considerably; in particular, the aqueous extracts from BB and BLA markedly reduced cell proliferation. p38 phosphorylation induced by BB aqueous extract increased the expression of cleaved caspase 3 and cleaved poly (ADP-ribose) polymerase (PARP), consequently inducing the apoptosis of A549 and H1299 cells. When the p38 inhibitor SB203580 was applied, phosphorylation of p38 decreased, and the expression of cleaved caspase 3 and cleaved PARP was also inhibited, resulting in a reduction of cell death. In addition, BB aqueous extracts reduced the secretion of MMP-9, leading to inhibition of cancer cell invasion. By contrast, after transfection of short hairpin RNA shMMP-9 (for a knockdown of MMP-9) into cancer cells, BB aqueous extracts treatment failed to suppress the cancer cell invasiveness. According to our results about their anticancer effects on NSCLC, probiotics consisting of Bifidobacterium species may be useful as adjunctive anticancer treatment in the future.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8265-8270
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• 2016

Purpose: A cost-utility analysis was performed to assess the cost-utility of neoadjuvant chemotherapy regimens containing doxorubicin and cyclophosphamide (AC) versus paclitaxel and gemcitabine (PG) for locally advanced breast cancer patients in Iran. Materials and Methods: This cross-sectional study in Namazi hospital in Shiraz, in the south of Iran covered 64 breast cancer patients. According to the random numbers, the patients were divided into two groups, 32 receiving AC and 32 PG. Costs were identified and measured from a community perspective. These items included medical and non-medical direct and indirect costs. In this study, a data collection form was used. To assess the utility of the two regimens, the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core30 (EORTC QLQ-C30) was applied. Using a decision tree, we calculated the expected costs and quality adjusted life years (QALYs) for both methods; also, the incremental cost-effectiveness ratio was assessed. Results: The results of the decision tree showed that in the AC arm, the expected cost was 39,170 US$ and the expected QALY was 3.39 and in the PG arm, the expected cost was 43,336 dollars and the expected QALY was 2.64. Sensitivity analysis showed the cost effectiveness of the AC and ICER=-5535 US$. Conclusions: Overall, the results showed that AC to be superior to PG in treatment of patients with breast cancer, being less costly and more effective.

• Journal of The Korea Institute of Healthcare Architecture
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• v.23 no.3
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• pp.57-69
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• 2017

Purpose: This study aims to emphasize interrelation between healthcare policies, design standards and hospital architecture of the United States since 1950s; to examine spatial characteristics of inpatient care facilities through case studies; and to consider the social implication of these spatial changes. Methods: In this study, reviewing the overall healthcare system, design standards and inpatient care facilities of the United States since 1950s, a total of five inpatient care facilities, one for each period, were selected in order to analyze the spatial characteristics. The spatial maps of Space Syntax were employed for analyzing five case studies. Results: The distance between the nursing station, the support service, and inpatient room were getting closer. The spatial structure of inpatient care facilities is transformed from tree structures to annular tree structures. This result shows that the efficiency between patient, staff and support service is higher and the depth of the spaces is getting deeper, which indicates that efficiency for improving healthcare quality affect the spatial structure of inpatient care facilities. Implications: In the future, if Korea's health policy is changed to a demand-oriented health care policy, this conclusion predicts medical planning of hospital will be focused on the efficiency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1296-1301
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• 2021

With the recent increase in diabetes incidence worldwide, research has been conducted to predict diabetes through various machine learning and deep learning technologies. In this work, we present a model for predicting diabetes using machine learning techniques with German Frankfurt Hospital data. We apply outlier handling using Interquartile Range (IQR) techniques and Pearson correlation and compare model-specific diabetes prediction performance with Decision Tree, Random Forest, Knn (k-nearest neighbor), SVM (support vector machine), Bayesian Network, ensemble techniques XGBoost, Voting, and Stacking. As a result of the study, the XGBoost technique showed the best performance with 97% accuracy on top of the various scenarios. Therefore, this study is meaningful in that the model can be used to accurately predict and prevent diabetes prevalent in modern society.