• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.1-7
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• 2018

As IT technology develops recently, medical equipment used in hospitals is demanding high performance. However, since the user visits different hospitals depending on the user's situation, the medical information treated at the hospital is distributed among the hospitals. In this paper, we propose a model to efficiently integrate the health care information of the users stored in the hospital in order to collect the healthcare information of the users who visited the different hospitals. The proposed model synchronizes users' healthcare information collected from personal wearable devices to collect user - centered healthcare information. In addition, the proposed model performs integrity and validity check related to user's healthcare information in a database existing in a cloud environment in order to smoothly share data with the healthcare service center. In particular, the proposed model enables tree - based data processing to smoothly manage healthcare information collected from mobile platforms.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.5 no.2
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• pp.186-191
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• 2002

Choledochal cyst is a congenital anomaly with classic triad of abdominal pain, jaundice and right upper abdominal mass. Bile peritonitis caused by cyst rupture is relatively not rare in infancy. The mechanism of rupture must be epithelial irritation of the biliary tract by refluxed pancreatic juice caused by pancreatico-biliary malunion associated with mural immaturity in infancy, rather than an abnormal rise in ductal pressure or congenital mural weakness at a certain point. We experienced a case of bile peritonitis caused by spontanenous rupture of choledochal cyst in a 10-month-old girl presented with abdominal distension, persistent fever, diarrhea, irritability and intractable ascites. She was presumed as having bile peritonitis by bile colored ascitic fluid with elevated bilirubin level and diagnosis was made by $^{99m}Tc$ DISIDA hepatobiliary scan showing extrahepatic biliary leak. The perforated cyst was surgically removed and the biliary tree was reconstructed with a Roux-en-Y hepaticojejunostomy.

• Journal of Trauma and Injury
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• v.34 no.4
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• pp.299-304
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• 2021

Atypical hemolytic uremic syndrome (aHUS) is a rare, progressive, life-threatening condition of thrombotic microangiopathy characterized by thrombocytopenia, microangiopathic hemolytic anemia, and renal impairment. The mechanisms underlying aHUS remain unclear. Herein, we present the first case in the literature of aHUS after a traumatic injury. A 55-year-old male visited the emergency department after a traumatic injury caused by a tree limb. Abdominal computed tomography revealed a rectal wall defect with significant air density in the perirectal space and preperitoneum, implying rectal perforation. Due to the absence of intraperitoneal intestinal perforation, we performed diverting sigmoid loop colostomy. An additional intermittent simple repair was performed due to perianal and anal injuries. One day postoperatively, his urine output abruptly decreased and serum creatinine level increased. His platelet level decreased, and a spiking fever occurred after 2 days. The patient was diagnosed with acute renal failure secondary to aHUS and was treated with fresh frozen plasma replacement. Continuous renal replacement therapy (CRRT) was also started for oliguria and uremic symptoms. The patient received CRRT for 3 days and intermittent hemodialysis thereafter. After hemodialysis and subsequent supportive treatment, his urine output and renal function improved. The hemolytic anemia and thrombocytopenia also gradually improved. Dialysis was terminated on day 22 of admission and the patient was discharged after recovery. This case suggests that that a traumatic event can trigger aHUS, which should be considered in patients who have thrombocytopenia and acute renal failure with microangiopathic hemolytic anemia. Early diagnosis and appropriate management are critical for favorable outcomes.

• Korean Journal of Head & Neck Oncology
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• v.38 no.1
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• pp.17-24
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• 2022

Background/Objectives: This study analyzed the prognostic significance of clinico-pathologic factors including comprehensive nodal factors in parotid gland cancers (PGCs) patients and constructed a survival prediction model for PGCs patients using machine learning techniques. Materials & Methods: A total of 131 PGCs patients were enrolled in the study. Results: There were 19 cases (14.5%) of lymph nodes (LNs) at the lower neck level and 43 cases (32.8%) involved multiple level LNs metastases. There were 2 cases (1.5%) of metastases to the contralateral LNs. Intraparotid LNs metastasis was observed in 6 cases (4.6%) and extranodal extension (ENE) findings were observed in 35 cases (26.7%). Lymphovascular invasion (LVI) and perineural invasion findings were observed in 42 cases (32.1%) and 49 cases (37.4%), respectively. Machine learning prediction models were constructed using clinico-pathologic factors including comprehensive nodal factors and Decision Tree and Stacking model showed the highest accuracy at 74% and 70% for predicting patient's survival. Conclusion: Lower level LNs metastasis and LNR have important prognostic significance for predicting disease recurrence and survival in PGCs patients. These two factors were used as important features for constructing machine learning prediction model. Our machine learning model could predict PGCs patient's survival with a considerable level of accuracy.

• Journal of Hospice and Palliative Care
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• v.25 no.2
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• pp.76-84
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• 2022

Purpose: We compared cost-effectiveness parameters between inpatient and home-based hospice-palliative care services for terminal cancer patients in Korea. Methods: A decision-analytic Markov model was used to compare the cost-effectiveness of hospice-palliative care in an inpatient unit (inpatient-start group) and at home (home-start group). The model adopted a healthcare system perspective, with a 9-week horizon and a 1-week cycle length. The transition probabilities were calculated based on the reports from the Korean National Cancer Center in 2017 and Health Insurance Review & Assessment Service in 2020. Quality of life (QOL) was converted to the quality-adjusted life week (QALW). Modeling and cost-effectiveness analysis were performed with TreeAge software. The weekly medical cost was estimated to be 2,481,479 Korean won (KRW) for inpatient hospice-palliative care and 225,688 KRW for home-based hospice-palliative care. One-way sensitivity analysis was used to assess the impact of different scenarios and assumptions on the model results. Results: Compared with the inpatient-start group, the incremental cost of the home-start group was 697,657 KRW, and the incremental effectiveness based on QOL was 0.88 QALW. The incremental cost-effectiveness ratio (ICER) of the home-start group was 796,476 KRW/QALW. Based on one-way sensitivity analyses, the ICER was predicted to increase to 1,626,988 KRW/QALW if the weekly cost of home-based hospice doubled, but it was estimated to decrease to -2,898,361 KRW/QALW if death rates at home doubled. Conclusion: Home-based hospice-palliative care may be more cost-effective than inpatient hospice-palliative care. Home-based hospice appears to be affordable even if the associated medical expenditures double.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.2037-2042
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• 2013

Background: Different methods of diagnosis have been found to be inefficient in terms of screening and early diagnosis of lung cancer. Cancer cells produce proteins whose serum levels may be elevated during the early stages of cancer development. Therefore, those proteins may be recognized as potential cancer markers. The aim of this study was to differentiate healthy individuals and lung cancer cases by analyzing their serum protein profiles and evaluate the efficacy of this method in the early diagnosis of lung cancer. Materials and Methods: 170 patients with lung cancer, 53 under high risk of lung cancer, and 47 healthy people were included in our study. Proteomic analysis of the samples was performed with the SELDI-TOF-MS approach. Results: The most discriminatory peak of the high risk group was 8141. When tree classification analysis was performed between lung cancer and the healthy control group, 11547 was determined as the most discriminatory peak, with a sensitivity of 85.5%, a specificity of 89.4%, a positive predictive value (PPV) of 96.7% and a negative predictive value (NPV) of 62.7%. Conclusions: We determined three different protein peaks 11480, 11547 and 11679 were only present in the lung cancer group. The 8141 peak was found in the high-risk group, but not in the lung cancer and control groups. These peaks may prove to be markers of lung cancer which suggests that they may be used in the early diagnosis of lung cancer.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.6
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• pp.2668-2676
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• 2011

In order to analyze the variation in length of stay(LOS) of injury inpatients, we developed severity-adjusted LOS model using Korean National Discharge In-depth Injury Survey data of Center for Disease Control. Appling this model, we calculated predicted values and, after standardizing LOS using the differences from the actual values, analyzed the variation in LOS. Major factors affecting severity-adjusted LOS of injury inpatients were found to be severity, surgery(or no surgery), age, injury mechanism and channel of hospitalization. Result of analysis of the differences between the actual values and predicted values adjusted by decision tree model suggested that there were statistically significant differences by hospital size(number of beds), type of insurance and location of institution. In order to reduce the variation in LOS, efforts should be exerted in developing nationwide treatment protocol, inducing medical institutions to utilize it, and furthermore systematically evaluating it to reduce the variation continually.

• Journal of Chest Surgery
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• v.24 no.1
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• pp.15-25
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• 1991

From February 1988 to December 1990, 42 patients underwent so called REV operation for pulmonary stenosis or atresia with or without anomalies of ventriculoarterial connection and truncus arteriosus. The principles of operative technique are mobilization of pulmonary arterial tree beyond the pericardial reflection, transection of pulmonary trunk between the pulmonary ventricle and pulmonary artery, suture of distal pulmonary arterial stump to the upper margin of Pulmonary ventriculotomy site with absorbable suture, and anterior patch with 0.625% glutaraldehyde fixed autologous pericardium with monocusp inside it. Age at operation ranged 3-156months [mean 41.8 month] with twelve of whom infants. Operative indications were pulmonary atresia, with ventricular septal defect[16], and pulmonary stenosis with double outlet right ventricle[8], with ventricular septal defect[16], with double outlet right ventricle[8], with complete transposition of the great arteries[8], with corrected transposition of the great arteries[6], with Fallot`s tetralogy[3], and truncus arteriosus[1]. There were six hospital deaths[14%] and no late death. Twenty-four of 36 survivals were followed up more than 12 months with good clinical results. Postoperative angiocardiogram was performed in fifteen patients. Hemodynamically, two patents had residual pressure gradients along the pulmonary outflow tract, one patient showed severe pulmonary regurgitation; morphologically, there were six significant stenosis of left pulmonary arterial tree, two of whom showed significant pressure gradients. Our present experience with REV operation suggests that this technique make it possible to perform anatomic repair in a wide variety of congenital anomalies of abnormal ventriculoarterial connection associated with pulmonary outflow tract obstruction without using the prosthetic material, even in infants, with relatively low mortality and morbidity.

• Journal of Korean Critical Care Nursing
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• v.11 no.3
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• pp.45-57
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• 2018

Purpose : This study was performed to identify the influencing factors of unplanned intensive care unit (ICU) readmission. Methods : The study adopted a Rretrospective case control cohort design. Data were collected from the electronic medical records of 844 patients who had been discharged from the ICUs of a university hospital in Incheon from June 2014 to December 2014. Results : The study found the unplanned ICU readmission rate was to be 6.4%(n=54). From the univariate analysis revealed that, major symptoms at $1^{st}$ ICU admission, severity at $1^{st}$ ICU admission (CPSCS and APACHE II), duration of applying ventilator application during $1^{st}$ ICU admission, severity at $1^{st}$ discharge from ICU (CPSCS, APACHE II, and GCS), and application of $FiO_2$ with oxygen therapy, implementation of sputum expectoration methods, and length of stay of ICU at $1^{st}$ ICU discharge were appeared to be significant; further, decision tree model analysis revealed that while only 4 variables (sputum expectoration methods, length of stay of ICU, $FiO_2$ with oxygen therapy at $1^{st}$ ICU discharge, and major symptoms at $1^{st}$ ICU admission) were shown to be significant. Conclusions : Since sputum expectoration method was the most important factor to predictor of unplanned ICU readmission, a assessment tool for the patients' capability of sputum expectoration needs to should be developed and implemented, and standardized ICU discharge criteria, including the factors identified from the by empirical evidences, might should be developed to decrease the unplanned ICU readmission rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.730-736
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• 2011

In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market(Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies(USA, MIT-BIH DB) were used for data base.