• Proceedings of the Korea Society for Simulation Conference
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• 2004.05a
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• pp.109-117
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• 2004

In this paper, we consider the aortic sinus baroreceptor, which is the most representative baroreceptor sensing the variance of pressure in the cardiovascular system, and propose heart activity control model to observe the effect of delay time in heart period and stroke volume under the regulation of baroreflex in the aortic sinus. The proposed heart activity baroreflex regulation model contains electric circuit sub-model. We constituted the time delay sub-model to observe sensitivity of heart activity baroreflex regulation model by using the variable value to represent the control signal transmission time from the output of baroreflex regulation model to efferent nerve through central nervous system. The simulation object of this model is to observe variability of the cardiovascular system by variable value in time delay sub-model. As simulation results, we observe three patterns of the cardiovascular system variability by the time delay, First, if the time delay over 2.5 second, aortic pressure and stroke volume and heart rate is observed nonperiodically and observed. Finally, if time delay under 0.1 second, then heart rate and aortic pressure-heart rate trajectory is maintained in stable state.

• Journal of Chest Surgery
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• v.54 no.2
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• pp.88-98
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• 2021

Background: This study aimed to develop a new risk prediction model for operative mortality in a Korean cohort undergoing heart valve surgery using the Korea Heart Valve Surgery Registry (KHVSR) database. Methods: We analyzed data from 4,742 patients registered in the KHVSR who underwent heart valve surgery at 9 institutions between 2017 and 2018. A risk prediction model was developed for operative mortality, defined as death within 30 days after surgery or during the same hospitalization. A statistical model was generated with a scoring system by multiple logistic regression analyses. The performance of the model was evaluated by its discrimination and calibration abilities. Results: Operative mortality occurred in 142 patients. The final regression models identified 13 risk variables. The risk prediction model showed good discrimination, with a c-statistic of 0.805 and calibration with Hosmer-Lemeshow goodness-of-fit p-value of 0.630. The risk scores ranged from -1 to 15, and were associated with an increase in predicted mortality. The predicted mortality across the risk scores ranged from 0.3% to 80.6%. Conclusion: This risk prediction model using a scoring system specific to heart valve surgery was developed from the KHVSR database. The risk prediction model showed that operative mortality could be predicted well in a Korean cohort.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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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).

• Journal of the Korean Society of Radiology
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• v.8 no.3
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• pp.117-121
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• 2014

The understanding of medical knowledge regarding cardiac shape and cardiovascular system is essentially required for a medical personnel as well as a medical technician. Correct acquisition of medical knowledge through cardiac miniature model is very critical because it is directly related to a patient life. therefore replica model is necessary for the education regarding cardiac and cardiovascular system. In this study anatomical cardiovascular system on fluoroscopy can be shown by cardiac and cardiovascular fluoroscopy replica model. Also, it helps to understand a cardiovascular radiograph. Consequentially medical personnel and medical technician are able to take advantage of this equipment to understand cardiovascular fluoroscopy system.

• International Journal of Vascular Biomedical Engineering
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• v.2 no.2
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• pp.16-26
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• 2004

The object of this study is to develop a mathematical model of the hemodialysis system including the mechanism of solute kinetics, water exchange and also cardiovascular dynamics. The cardiovascular system model used in this study simulates the short-term transient and steady-state hemodynamic responses such as hypotension and disequilibrium syndrome (which are main complications to hemodialysis patients) during hemodialysis. It consists of a 12 lumped-parameter representation of the cardiovascular circulation connected to set-point models of the arterial baroreflexes, a kinetic model (hemodialysis system model) with 3 compartmental body fluids and 2 compartmental solutes. We formulate mathematically this model in terms of an electric analog model. All resistors and most capacitors are assumed to be linear. The control mechanisms are mediated by the information detected from arterial pressoreceptors, and they work on systemic arterial resistance, heart rate, and systemic venous unstressed volume. The hemodialysis model includes the dynamics of urea, creatinine, sodium and potassium in the intracellular and extracellular pools as well as fluid balance equations for the intracellular, interstitial, and plasma volumes. Model parameters are largely based on literature values. We have presented the results on the simulations performed by changing some model parameters with respect to their basal values. In each case, the percentage changes of each compartmental pressure, heart rate (HR), total systemic resistance (TSR), ventricular compliance, zero pressure filling volume and solute concentration profiles are represented during hemodialysis.

• Journal of the Korean Mathematical Society
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• v.43 no.4
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• pp.885-897
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• 2006

A new mathematical model of pumping heart coupled to lumped compartments of blood circulation is presented. This lumped pulsatile cardiovascular model consists of eight compartments of the body that include pumping heart, the systemic circulation, and the pulmonary circulation. The governing equations for the pressure and volume in each vascular compartment are derived from the following equations: Ohm's law, conservation of volume, and the definition of compliances. The pumping heart is modeled by the time-dependent linear curves of compliances in the heart. We show that the numerical results in normal case are in agreement with corresponding data found in the literature. We extend the developed lumped model of circulation in normal case into a specific model for arrhythmia. These models provide valuable tools in examining and understanding cardiovascular diseases.

• Research in Community and Public Health Nursing
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• v.17 no.2
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• pp.153-165
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• 2006

Purpose: The purpose of this study was to develop and test a structural model on cardiovascular disease risk factors among male manufacturing workers. Methods: Data were collected through questionnaires and health exams from 201 workers in a local electronic company during September 2004. Data analysis was done with SAS 9.1 for descriptive statistics and PC-LISREL 8.54 for covariance structural analysis Results: The overall fit of the hypothetical model to the data was moderate, it was modified by deleting five paths. The modified model had a better fit to the data($x^2=504.23$(p<001, df: 180), $x^2/df=2.80$, GFI=.95, RMR=.07, NFI=.90, PGFI=.64). Health behaviors and psychosocial distress were found to have significant direct effects on the cardiovascular disease risk factors. Self-concept had direct effect on psychosocial distress or health behaviors. Self-concept, work environment, and work condition had direct effect on social support. Work environment had indirect effect on psychosocial distress. Social support had indirect effect on health behaviors. But work environment and work condition were found to have little direct effect on health behaviors, psychosocial distress or cardiovascular disease risk factors. Conclusion: A cardiovascular health promotion program should therefore include psycho-social factors as well as health behavioral determinants in worksites.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.713-716
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• 2002

Aging effect on the cardiovascular circulation is simulated by lumped parameter model. Aging phenomena can be hemodynamically explained as (1) the increase of flow resistance induced by remodeling of artery vessels and increased viscosity of blood and (2) the reduction of the vessel capacitance caused by arteriosclerosis. Appropriate physiological parameters are evaluated from the clinical data of adults and old men. Simulation results well explain the hypertension with aging of cardiovascular system.

• Journal of Biomedical Engineering Research
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• v.22 no.5
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• pp.393-402
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• 2001

A computational model representative of cardiovascular circulation was built using 12 standard lumped compartments. Especially, both the baroreceptor reflex and the cardiopulmonary reflex control model were implemented to explain the auto-regulation of cardiovascular system. Another important aspect of this model is to utilize the impulse-response curve of the nerve system in transferring the impulse error signals to autonomous nerve system. For the verification of this model, we have computed the normal hemodynamic conditions and compared those with the clinical data. Then. hemodynamic shock of 20% hemorrhage to cardiovascular system was simulated to test the effects of the control system model. The results of these two simulations were well matched with the experimental ones. The steady state LBNP simulation was also performed. The transient changes of hemodynamic variables due to ramp increase of bias pressure of LBNP showed good agreement with the physiological experiments. Numerical solution using only the baroreflex model showed relatively a larger deviation from the experimental data. compared with the one using the control model haying both the baroreflex and the cardiopulmonary reflex systems, which shows an important role of the cardiopulmonary reflex system for the simulation of the hemodynamic behavior of the cardiovascular system .

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.542-546
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• 2008

In this study, we developed a whole cardiovascular system model combined with a Laplace heart based on the numerical cardiac cell model and a detailed arterial network structure. The present model incorporates the Laplace heart model and pulmonary model using the lumped parameter model with the distributed arterial system model. The Laplace heart plays a role of the pump consisted of the atrium and ventricle. We applied a cellular contraction model modulated by calcium concentration and action potential in the single cell. The numerical arterial model is based upon a numerical solution of the one-dimensional momentum equations and continuity equation of flow and vessel wall motion in a geometrically accurate branching network of the arterial system including energy losses at bifurcations. For validation of the present method, the computed pressure waves are compared with the existing experimental observations. Using the cell-system-arterial network combined model, the pathophysiological events from cells to arterial network are delineated.