• Clinical and Experimental Pediatrics
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• 제59권10호
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• pp.408-413
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• 2016

Purpose: This study investigated predictors of unresponsiveness to second-line intravenous immunoglobulin (IVIG) treatment for Kawasaki disease (KD). Methods: This was a single-center analysis of the medical records of 588 patients with KD who had been admitted to Asan Medical Center between 2006 and 2014. Related clinical and laboratory data were analyzed by univariate and multivariate logistic regression analyses. Results: Eighty (13.6%) of the 588 patients with KD were unresponsive to the initial IVIG treatment and received a second dose. For these 80 patients, univariate analysis of the laboratory results obtained before administering the second-line IVIG treatment showed that white blood cell count, neutrophil percent, hemoglobin level, platelet count, serum protein level, albumin level, potassium level, and C-reactive protein level were significant predictors. The addition of methyl prednisolone to the second-line regimen was not associated with treatment response (odds ratio [OR], 0.871; 95% confidence interval [CI], 0.216-3.512; P=0.846). Multivariate analysis revealed serum protein level to be the only predictor of unresponsiveness to the second-line treatment (OR, 0.160; 95% CI, 0.028-0.911; P=0.039). Receiver operating characteristic curve analysis to determine predictors of unresponsiveness to the second dose of IVIG showed a sensitivity of 100% and specificity of 72% at a serum protein cutoff level of <7.15 g/dL. Conclusion: The serum protein level of the patient prior to the second dose of IVIG is a significant predictor of unresponsiveness. The addition of methyl prednisolone to the second-line regimen produces no treatment benefit.

• Korean Journal of Artificial Intelligence
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• 제11권3호
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• Laboraroty Animal Research
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• 제34권4호
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• pp.248-256
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• 2018

O-2-$^{18}F$-fluoroethyl-l-tyrosine ($[^{18}F]FET$) has been widely used for glioblastomas (GBM) in clinical practice, although evaluation of its applicability in non-clinical research is still lacking. The objective of this study was to examine the value of $[^{18}F]FET$ for treatment evaluation and prognosis prediction of anti-angiogenic drug in an orthotopic mouse model of GBM. Human U87MG cells were implanted into nude mice and then bevacizumab, a representative anti-angiogenic drug, was administered. We monitored the effect of anti-angiogenic agents using multiple imaging modalities, including bioluminescence imaging (BLI), magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET/CT). Among these imaging methods analyzed, only $[^{18}F]FET$ uptake showed a statistically significant decrease in the treatment group compared to the control group (P=0.02 and P=0.03 at 5 and 20 mg/kg, respectively). This indicates that $[^{18}F]FET$ PET is a sensitive method to monitor the response of GBM bearing mice to anti-angiogenic drug. Moreover, $[^{18}F]FET$ uptake was confirmed to be a significant parameter for predicting the prognosis of anti-angiogenic drug (P=0.041 and P=0.007, on Days 7 and 12, respectively, on Pearson's correlation; P=0.048 and P=0.030, on Days 7 and 12, respectively, on Cox regression analysis). However, results of BLI or MRI were not significantly associated with survival time. In conclusion, this study suggests that $[^{18}F]FET$ PET imaging is a pertinent imaging modality for sensitive monitoring and accurate prediction of treatment response to anti-angiogenic agents in an orthotopic model of GBM.

• Journal of Microbiology
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• 제45권5호
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• pp.441-446
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• 2007

HIV-1 coreceptor usage and phenotype mainly determined by V3 loop are associated with the disease progression of AIDS. Predicting HIV-1 coreceptor usage and phenotype facilitates the monitoring of R5-to-X4 switch and treatment decision-making. In this study, we employed random forest to predict HIV-1 biological phenotype, based on 37 random features of V3 loop. In comparison with PSSM method, our RF predictor obtained higher prediction accuracy (95.1% for coreceptor usage and 92.1% for phenotype), especially for non-B non-C HIV-l subtypes (96.6% for coreceptor usage and 95.3% for phenotype). The net charge, polarity of V3 loop and five V3 sites are seven most important features for predicting HIV-1 coreceptor usage or phenotype. Among these features, V3 polarity and four V3 sites (22, 12, 18 and 13) are first reported to have high contribution to HIV-1 biological phenotype prediction.

• Asian Pacific Journal of Cancer Prevention
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• 제16권15호
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• pp.6729-6734
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• 2015

Background: To analyze the mortality distribution of nasopharyngeal carcinoma in China from 1991 to 2013, to predict the mortality in the ensuing five years, and to provide evidence for prevention and treatment of nasopharyngeal carcinoma. Materials and Methods: Mortality data for Nasopharyngeal Carcinoma in China from 1991 to 2013 were used to describe its epidemiological characteristics, such as the change of the standardized mortality rate, sex and age differences, urban-rural differences. Trend-surface analysis was used to study the geographical distribution of the mortality. Curve estimation, time series, gray modeling, and joinpoint regression were used to predict the mortality for the ensuing five years in the future. Results: In China, the standardized mortality rate of Nasopharyngeal Carcinoma increased with time from 1996, reaching the peak values of $1.45/10^5$ at the year of 2002, and decreased gradually afterwards. With males being 1.51 times higher than females, and the city had a higher rate than the rural during the past two decades. The mortality rate increased from age 40. Geographical analysis showed the mortality rate increased from middle to southern China. Conclusions: The standardized mortality rate of Nasopharyngeal Carcinoma is falling. The regional disease control for Nasopharyngeal Carcinoma should be focused on Guangdong province of China, and the key targets for prevention and treatment are rural men, especially after the age of 40. The mortality of Nasopharyngeal Carcinoma will decrease in the next five years.

• Journal of Radiation Protection and Research
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• 제36권1호
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• pp.1-7
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• 2011

A simple mathematical model of simultaneous combined action of environmental agents has been proposed to describe the synergistic interaction of microwave and high ambient temperature treatment on animal heating. The model suggests that the synergism is caused by the additional effective damage arising from an interaction of sublesions induced by each agent. These sublesions are considered to be ineffective if each agent is taken individually. The additional damage results in a higher body temperature increment when compared with that expected for an independent action of each agent. The model was adjusted to describe the synergistic interaction, to determine its greatest value and the condition under which it can be achieved. The prediction of the model was shown to be consistent with experimental data on rabbit heating. The model appears to be appropriate and the conclusions are valid.

• Clinical Psychopharmacology and Neuroscience
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• 제16권4호
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• pp.461-468
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• 2018

Objective: Cognitive disturbance is one of the major symptoms of depression and may be improved by treatment with antidepressants. This study aimed to investigate the predictors of cognitive improvement in patients with major depressive disorder (MDD) who were taking antidepressants. Methods: This study included 86 patients with MDD who completed 12 weeks of antidepressant monotherapy. Cognitive symptoms were assessed using the Perceived Deficits Questionnaire-Korean version (PDQ-K), which addresses four domains of cognitive functioning (attention/concentration, retrospective memory, prospective memory, and organization/planning) and was administered at study entry and at the 12-week end point. A variety of demographic, clinical, and treatment-related variables were evaluated as predictors of changes in total and domain scores. Results: All PDQ-K domains showed significant improvement after 12 weeks of antidepressant treatment. More severe initial depressive symptoms, fewer sick-leave days at study entry, and reduced use of concomitant anxiolytics/hypnotics during treatment were significantly associated with greater cognitive improvement. Conclusion: Cognitive symptoms are more responsive to antidepressant treatment in patients with severe MDD. Reduced use of anxiolytics and hypnotics could improve the cognitive functioning of patients with MDD taking antidepressants.

• Journal of Korea Entertainment Industry Association
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• 제4권4호
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• pp.72-76
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• 2010

Clinical nomogram is a graphical representation of numeric formula, constructed from clinical cases database of followed patients' treatment, which is used for medical predication. For a clinical nomogram to contribute patient care, it is required to accumulate as many as clinical cases and to extract medical prediction knowledge. It needs to be equipped with an effective method to build medical nomogram with high predication accuracy. It is desirable for medical nomogram to be accessible at patient care point. This paper proposes a medical nomogram service system architecture which takes into account the above-mentioned issues. The proposed system architecture includes a web-based database subsystem to maintain and keep track of clinical cases. On the periodic basis, a new clinical nomogram is reconstructed for the updated clinical database. For the convenient use of patient care practice environment, an app-based program is provided which makes prediction based on the most recent clinical nomogram constructed in the service system. The proposed method has been applied to a clinical nomogram service system development for recurrence and survival prediction in bladder cancer patients.

• Proceedings of the Korean Society of Medical Physics Conference
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• 한국의학물리학회 2002년도 Proceedings
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• pp.429-431
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• 2002

Metabolic analysis of biological tissues, the interventional radiology in MRT (Magnetic Resonance Treatment) and for clinical diagnoses, representation of 4-Dimensional (4D) structural information (x,y,z,t) of biological tissues is required. This paper discusses image representation techniques for those 4D MR Images. We have proposed an image reconstruction method for ultra-fast 3D MRI. It is based on image interpolation and prediction of un-acquired pictorial data in both of the real and the k-space (the acquisition domain in MRI). A 4D MR image is reconstructed from only two 3D MR images and acquired a few echo signals that are optimized by prediction of the tissue motion. This prediction can be done by the phase of acquired echo signal is proportioned to the tissue motion. On the other hand, reconstructed 4D MR images are represented as a 3D-movie by using computer graphics techniques. Rendered tissue surfaces and/or ROIs are displayed on a CRT monitor. It is represented in an arbitrary plane and/or rendered surface with their motion. As examples of the proposed representation techniques, the finger and the lung motion of healthy volunteers are demonstrated.

• Journal of Intelligence and Information Systems
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• 제28권1호
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• pp.217-242
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• 2022

With the launch of Artificial Intelligence(AI)-based intelligent products on the market, innovative changes are taking place not only in business but also in consumers' daily lives. Intelligent products have the potential to realize technology differentiation and increase market competitiveness through advanced functions of artificial intelligence. However, there is no new product development methodology that can sufficiently reflect the characteristics of artificial intelligence for the purpose of developing intelligent products with high market acceptance. This study proposes a KANO-QFD integrated model as a methodology for intelligent product development. As a specific example of the empirical analysis, the types of consumer requirements for hair loss prediction and treatment device were classified, and the relative importance and priority of engineering characteristics were derived to suggest the direction of intelligent medical product development. As a result of a survey of 130 consumers, accurate prediction of future hair loss progress, future hair loss and improved future after treatment realized and viewed on a smartphone, sophisticated design, and treatment using laser and LED combined light energy were realized as attractive quality factors among the KANO categories. As a result of the analysis based on House of Quality of QFD, learning data for hair loss diagnosis and prediction, micro camera resolution for scalp scan, hair loss type classification model, customized personal account management, and hair loss progress diagnosis model were derived. This study is significant in that it presented directions for the development of artificial intelligence-based intelligent medical product that were not previously preceded.