• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.303-308
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• 2017

Nowadays, living standard is improved and people have high interest to the personal health care problem. Accordingly, people desire to know the personal physical condition and the related medical treatment. Thus, there is the necessary of the personalized medical treatment, and there are many studies about the automatic disease diagnosis and the related services. Those studies focus on the particular disease prediction which is based on the related particular data. However, there is no studies about the medical treatment prediction. In our study, national health data based medical treatment predictor is built by using SVM, and the performance is evaluated by comparing with other prediction methods. The experimental results show that the health data based medical treatment prediction resulted in the average accuracy of 80%, and the SVM performs better than other prediction algorithms.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.113-118
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• 1991

Hypertension is a medical problem with no permanent cure. Extended hypertension can cause various cardio vascular diseases, cerebral vascular diseases, and circulatory system trouble. Medical treatment at present does not consider circadian variation of blood pressure in patients ; therefore, the problem of over-reduction of blood pressure through drugs sometimes occurs. This paper presents a prediction model of circadian variation or moon blood pressure employing the endocrine grand and the autonomic nervous system.

• Convergence Security Journal
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• v.23 no.1
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• pp.89-95
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• 2023

In order to help reduce the medical expenses of patients with auto insurance accidents, this study predicted the treatment period, which is the most important factor in the medical expenses of patients in their 40s and 50s, and analyzed the factors affecting the treatment period. To this end, a mechine learning model using five algorithms such as Decision Tree was created, and its performance was compared and analyzed between models. There were three algorithms that showed good performance including Decison Tree, Gradient Boost, and XGBoost. In addition, as a result of analyzing the factors affecting the prediction of the treatment period, the type of hospital, the treatment area, age, and gender were found. Through these studies, easy research methods such as the use of AutoML were presented, and we hope that the results of this study will help policies to reduce medical expenses for automobile insurance accidents.

• Journal of Information Technology Applications and Management
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• v.23 no.2
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• pp.263-277
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• 2016

Recently, 3D printing technology has been considered as a core applicable technology because it brings many improvements such as the development of medical technology, medical customization, and reducing production cost and shortening treatment period. This research suggests a market prediction framework for medical 3D printing business. As an immature market situation, it is important to control some uncertainty for market prediction such as a customers' conversion rate. So we adopt decision making tree (DMT) model which used to choose an optimal decision making among diverse pathway. Among medical industries this paper just focuses on dentistry business. For predicting a 5 year period trend expected market size, we identified some replaceable denture procedure by 3D printing, collected related data, controlled uncertain variables. The result shows that medical 3D printing business could be a market of 28.2 billion won at 1st year and in the end of fifth year it could become on a scale of 61.1 billion won market.

• The Journal of Korean Physical Therapy
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• v.21 no.2
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• pp.65-70
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• 2009

Purpose: The pathophysiology of congenital muscular torticollis (CMT) is that the sternoclavicularmastoid (SCM) is shortened on the involved side by fibrosis, leading to an ipsilateral tilt and contralateral rotation of the face and chin. The aim of this study was to examine the effect of physical therapy and develop a mass diameter prediction model for infants with CMT. Methods: Fifty six patients were diagnosed with CMT between April 2003 and December 2008. Infants with neurological complications, and spasmodic and ocular torticollis were excluded. Physical therapy was applied to those masses in the SCM muscles of those infants after checking their physical findings and the diameter of the mass with ultrasonography. Their physical findings and mass diameter was reevaluated when their neck tilt was under $5^{\circ}$. Results: The mean age when physical therapy was started was 35 days. After a mean 90 days of treatment, the subjects showed improvement in the neck tilt. Subjects whose neck tilted above $15^{\circ}$ showed significant improvement in neck tilt decreased their mass diameter (p<0.01). Facial symmetric infants showed a shorter recovery duration than the facial asymmetric infants (p<0.05). A mass decreasing model based on the diameter of the mass, facial symmetry or not and the physical therapy start day after birth was developed by linear regression. Conclusion: Physical therapy is an effective treatment for CMT. The change in the diameter of the mass on the SCM muscles after treatment can be predicted.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5589-5594
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• 2015

Cystitis and proctitis are defined as inflammation of bladder and rectum respectively. Haemorrhagic cystitis is the most severe clinical manifestation of radiation and chemical cystitis. Radiation proctitis and cystitis are major complications following radiotherapy. Prevention of radiation-induced haemorrhagic cystitis has been investigated using various oral agents with minimal benefit. Bladder irrigation remains the most frequently adopted modality followed by intra-vesical instillation of alum or formalin. In intractable cases, surgical intervention is required in the form of diversion ureterostomy or cystectomy. Proctitis is more common in even low dose ranges but is self-limiting and improves on treatment interruption. However, treatment of radiation proctitis is broadly non-invasive or invasive. Non-invasive treatment consists of non-steroid anti-inflammatory drugs (NSAIDs), anti-oxidants, sucralfate, short chain fatty acids and hyperbaric oxygen. Invasive treatment consists of ablative procedures like formalin application, endoscopic YAG laser coagulation or argon plasma coagulation and surgery as a last resort.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.510-518
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• 2022

This paper confirmed the technical reliability of mobile-based sarcopenia prediction and monitoring system. In implementing the developed system, we designed using only sensors built into a smartphone without a separate external device. The prediction system predicts the possibility of sarcopenia without visiting a hospital by performing the SARC-F survey, the 5-time chair stand test, and the rapid tapping test. The Monitoring system tracks and analyzes the average walking speed in daily life to quickly detect the risk of sarcopenia. Through this, it is possible to rapid detection of undiagnosed risk of undiagnosed sarcopenia and initiate appropriate medical treatment. Through prediction and monitoring system, the user may predict and manage sarcopenia, and the developed system can have a positive effect on reducing medical demand and reducing medical costs. In addition, collected data is useful for the patient-doctor communication. Furthermore, the collected data can be used for learning data of artificial intelligence, contributing to medical artificial intelligence and e-health industry.

• 대한방사선방어학회:학술대회논문집
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• 2009.11a
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• pp.116-117
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• 2009

• Clinical and Experimental Pediatrics
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• v.59 no.10
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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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• 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).