• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.4109-4115
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• 2014

Background: Accurate assessment of disease progression requires proper understanding of natural disease process which is often hidden and unobservable. For this purpose, disease status should be clearly detected. But in most diseases it is not possible to detect such status. This study, therefore, aims to present a model which both investigates the unobservable disease process and considers the error probability in diagnosis of disease states. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at the Iran Cancer Institute from 1995 to 1999 were analyzed. Moreover, to estimate and assess the effect of demographic, diagnostic and clinical factors as well as medical and post-surgical variables on transition rates and the probability of misdiagnosis of relapse, a hidden Markov multi-state model was employed. Results: Classification errors of patients in alive state without a relapse ($e_{21}$) and with a relapse ($e_{12}$) were 0.22 (95% CI: 0.04-0.63) and 0.02 (95% CI: 0.00-0.09), respectively. Only variables of age and number of renewed treatments affected misdiagnosis of relapse. In addition, patient age and distant metastasis were among factors affecting the occurrence of relapse (state1${\rightarrow}$state2) while the number of renewed treatments and the type and extent of surgery had a significant effect on death hazard without relapse (state2${\rightarrow}$state3)and death hazard with relapse (state2${\rightarrow}$state3). Conclusions: A hidden Markov multi-state model provides the possibility of estimating classification error between different states of disease. Moreover, based on this model, factors affecting the probability of this error can be identified and researchers can be helped with understanding the mechanisms of classification error.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8567-8571
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• 2016

Background: The Cox model is known as one of the most frequently-used methods for analyzing survival data. However, in some situations parametric methods may provide better estimates. In this study, a Weibull parametric model was employed to assess possible prognostic factors that may affect the survival of patients with breast cancer. Materials and Methods: We studied 438 patients with breast cancer who visited and were treated at the Cancer Research Center in Shahid Beheshti University of Medical Sciences during 1992 to 2012; the patients were followed up until October 2014. Patients or family members were contacted via telephone calls to confirm whether they were still alive. Clinical, pathological, and biological variables as potential prognostic factors were entered in univariate and multivariate analyses. The log-rank test and the Weibull parametric model with a forward approach, respectively, were used for univariate and multivariate analyses. All analyses were performed using STATA version 11. A P-value lower than 0.05 was defined as significant. Results: On univariate analysis, age at diagnosis, level of education, type of surgery, lymph node status, tumor size, stage, histologic grade, estrogen receptor, progesterone receptor, and lymphovascular invasion had a statistically significant effect on survival time. On multivariate analysis, lymph node status, stage, histologic grade, and lymphovascular invasion were statistically significant. The one-year overall survival rate was 98%. Conclusions: Based on these data and using Weibull parametric model with a forward approach, we found out that patients with lymphovascular invasion were at 2.13 times greater risk of death due to breast cancer.

• Annals of Geriatric Medicine and Research
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• v.20 no.3
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• pp.125-130
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• 2016

Background: Conventionally, elderly hip fracture patients are assessed by orthopedists to decide whether they need geriatric intervention. We aimed to evaluate the effect of perioperative geriatric intervention on healthcare outcomes in patients undergoing surgery for hip fractures. Methods: Our care model for hip fracture surgery resembles a combination of a routine geriatric consultation model and a geriatric ward model. We retrospectively reviewed the medical records of patients aged ${\geq}65years$ undergoing surgery for hip fracture at a single tertiary hospital from January 2010 to December 2013. We assessed comorbidity, indwelling status, fracture type, and mode of anesthesia. We also evaluated in-hospital expenditure, duration of admission, disposition at discharge and 1-year mortality as clinical outcomes. We developed a propensity score model using the variables of age, cholesterol, and creatinine and examined the effect of perioperative geriatric intervention on intergroup differences of clinical variables. Results: Among 639 patients, 138 patients received the geriatric intervention and 501 patients received the usual care. Univariate analysis showed that factors such as age; Charlson comorbidity index; and serum levels of cholesterol, albumin, and creatinine differed significantly between these 2 groups. There was no significant difference between the groups in terms of 1-year mortality, disposition at discharge, and in-hospital expenditure in the propensity matched model. However, the duration of hospitalization was shorter in the intervention group ($8.9{\pm}0.8days$) than in the usual care group ($14.2{\pm}3.7days$, p=0.006). Conclusion: This care model of geriatric intervention for patients with hip fracture is associated with reduced hospitalization duration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.16-37
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• 2022

Accurate liver segment segmentation based on radiological images is indispensable for the preoperative analysis of liver tumor resection surgery. However, most of the existing segmentation methods are not feasible to be used directly for this task due to the challenge of exact edge prediction with some tiny and slender vessels as its clinical segmentation criterion. To address this problem, we propose a novel deep learning based segmentation model, called Boundary-Aware Dual Attention Liver Segment Segmentation Model (BADA). This model can improve the segmentation accuracy of liver segments with enhancing the edges including the vessels serving as segment boundaries. In our model, the dual gated attention is proposed, which composes of a spatial attention module and a semantic attention module. The spatial attention module enhances the weights of key edge regions by concerning about the salient intensity changes, while the semantic attention amplifies the contribution of filters that can extract more discriminative feature information by weighting the significant convolution channels. Simultaneously, we build a dataset of liver segments including 59 clinic cases with dynamically contrast enhanced MRI(Magnetic Resonance Imaging) of portal vein stage, which annotated by several professional radiologists. Comparing with several state-of-the-art methods and baseline segmentation methods, we achieve the best results on this clinic liver segment segmentation dataset, where Mean Dice, Mean Sensitivity and Mean Positive Predicted Value reach 89.01%, 87.71% and 90.67%, respectively.

• Journal of Biomedical Engineering Research
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• v.33 no.3
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• pp.148-154
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• 2012

Hemorrhagic shock is a primary cause of deaths resulting from injury in the world. Although many studies have tried to diagnose accurately hemorrhagic shock in the early stage, such attempts were not successful due to compensatory mechanisms of humans. The objective of this study was to construct a survival prediction model of rats in acute hemorrhagic shock using a random forest (RF) model. Heart rate (HR), mean arterial pressure (MAP), respiration rate (RR), lactate concentration (LC), and peripheral perfusion (PP) measured in rats were used as input variables for the RF model and its performance was compared with that of a logistic regression (LR) model. Before constructing the models, we performed 5-fold cross validation for RF variable selection, and forward stepwise variable selection for the LR model to examine which variables were important for the models. For the LR model, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (ROC-AUC) were 0.83, 0.95, 0.88, and 0.96, respectively. For the RF models, sensitivity, specificity, accuracy, and AUC were 0.97, 0.95, 0.96, and 0.99, respectively. In conclusion, the RF model was superior to the LR model for survival prediction in the rat model.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.12
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• pp.5081-5084
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• 2015

Background: Breast cancer is a malignant tumor that starts from cells of the breast and is seen mainly in women. It's the most common cancer in women worldwide and is a major threat to health. The purpose of this study was to fit a Cox proportional hazards model for prediction and determination of years of survival in Iranian patients. Materials and Methods: A total of 366 patients with breast cancer in the Cancer Research Center were included in the study. A Cox proportional hazard model was used with variables such as tumor grade, number of removed positive lymph nodes, human epidermal growth factor receptor 2 (HER2) expression and several other variables. Kaplan-Meier curves were plotted and multi-years of survival were evaluated. Results: The mean age of patients was 48.1 years. Consumption of fatty foods (p=0.033), recurrence (p<0.001), tumor grade (p=0.046) and age (p=0.017) were significant variables. The overall 1- year, 3-year and 5-year survival rates were found to be 93%, 75% and 52%. Conclusions: Use of covariates and the Cox proportional hazard model are effective in predicting the survival of individuals and this model distinguished 4 effective factors in the survival of patients.

• Tuberculosis and Respiratory Diseases
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• v.82 no.2
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• pp.166-172
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• 2019

Background: The purpose of this study was to determine whether components of the ProVent model can predict the high medical costs in Korean patients requiring at least 21 days of mechanical ventilation (prolonged mechanical ventilation [PMV]). Methods: Retrospective data from 302 patients (61.6% male; median age, 63.0 years) who had received PMV in the past 5 years were analyzed. To determine the relationship between medical cost per patient and components of the ProVent model, we collected the following data on day 21 of mechanical ventilation (MV): age, blood platelet count, requirement for hemodialysis, and requirement for vasopressors. Results: The mortality rate in the intensive care unit (ICU) was 31.5%. The average medical costs per patient during ICU and total hospital (ICU and general ward) stay were 35,105 and 41,110 US dollars (USD), respectively. The following components of the ProVent model were associated with higher medical costs during ICU stay: age <50 years (average 42,731 USD vs. 33,710 USD, p=0.001), thrombocytopenia on day 21 of MV (36,237 USD vs. 34,783 USD, p=0.009), and requirement for hemodialysis on day 21 of MV (57,864 USD vs. 33,509 USD, p<0.001). As the number of these three components increased, a positive correlation was found betweeen medical costs and ICU stay based on the Pearson's correlation coefficient (${\gamma}$) (${\gamma}=0.367$, p<0.001). Conclusion: The ProVent model can be used to predict high medical costs in PMV patients during ICU stay. The highest medical costs were for patients who required hemodialysis on day 21 of MV.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.177-193
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• 2018

The business process management system that can integrate and manage a medical institution's processes has been increased importance to adapt to constantly changing medical environments and share information with various external medical institutions. The business process management system is an automated process tool that converts the graphic-based BPMN process model into a WS-BPEL, the execution language of the web service environment, and executes it through the process engine. However, the BPMN process model can be difficult to convert into WS-BPEL due to several ambiguities and structural inconsistencies. The process model may also contain structural anomalies that can lead to execution errors during process execution. In this paper, we present business process model formalization and structural anomaly verification techniques for facilitating integrated process management in medical institutions. Through the case study based on the IHE profile, we presented a formalized BPMN process model and verify the structural anomalies. We show the superiority of the proposed technique through comparative experiments with other related works.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.707-712
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• 2015

Background: To evaluate use of magnetic resonance imaging (MRI) and a logistic model including risk factors for lymph node metastasis for improved diagnosis. Materials and Methods: The subjects were 176 patients with rectal cancer who underwent preoperative MRI. The longest lymph node diameter was measured and a cut-off value for positive lymph node metastasis was established based on a receiver operating characteristic (ROC) curve. A logistic model was constructed based on MRI findings and risk factors for lymph node metastasis extracted from logistic-regression analysis. The diagnostic capabilities of MRI alone and those of the logistic model were compared using the area under the curve (AUC) of the ROC curve. Results: The cut-off value was a diameter of 5.47 mm. Diagnosis using MRI had an accuracy of 65.9%, sensitivity 73.5%, specificity 61.3%, positive predictive value (PPV) 62.9%, and negative predictive value (NPV) 72.2% [AUC: 0.6739 (95%CI: 0.6016-0.7388)]. Age (<59) (p=0.0163), pT (T3+T4) (p=0.0001), and BMI (<23.5) (p=0.0003) were extracted as independent risk factors for lymph node metastasis. Diagnosis using MRI with the logistic model had an accuracy of 75.0%, sensitivity 72.3%, specificity 77.4%, PPV 74.1%, and NPV 75.8% [AUC: 0.7853 (95%CI: 0.7098-0.8454)], showing a significantly improved diagnostic capacity using the logistic model (p=0.0002). Conclusions: A logistic model including risk factors for lymph node metastasis can improve the accuracy of MRI diagnosis of rectal cancer.