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BackgroundKidney transplantation is the best alternative treatment for end-stage renal disease (ESRD). In order to optimal use of donated kidneys, graft predicted survival can be used as a factor to allocate kidneys. The performance of prediction techniques is highly dependent on the correct selection of predictors. Hence, the main objective of this research is to propose a novel method for ranking the effective variables for predicting the kidney transplant survival.MethodsFive classification models were used to classify kidney recipients in long-and short-term survival classes. Synthetic minority oversampling (SMOTE) and random under sampling (RUS) were used to overcome the imbalanced class problem. In dealing with missing values, 2 appro
Most studies in the literature have focused on past behavior of customers to measure customer lifetime value, however, the rapid developments of technology and products make new conditions that cannot be predicted by past records anymore. In the era of new media and social networks, customers’ needs and expectations change fast which lead to instability of customer lifetime value.In the present study, we studied the dynamics of bank customers through value segments using big data analytics. By mining patterns of associations between customer transitions, we found six major categories, including the pattern of Local Leaders whose transitions are repeated by some follower groups within next two periods. Such results suggest that the dynamic
Purpose:This study aims to investigate the association of different risk factors including vitamin-D level with catheter-related-thrombosis in hemodialysis patients by applying data mining techniques.Methods:This study used the retrospectively approach and was done based on the CRISP-DM framework. The data of 1048 hemodialysis patients of Hasheminejad Kidney Center whose first catheterization was between 2014 and 2019 was used for analysis. In this study, patients with a previous history of deep venous thrombosis, thrombophilic condition, and undergone anticoagulant therapy were excluded. The decision tree J48 in WEKA software was used for modeling. The K-fold cross-validation method was also used to evaluate the classification performance.
Stillbirth is defined as fetal loss in pregnancy beyond 28 weeks by WHO. In this study, a machine-learning based method is proposed to predict stillbirth from livebirth and discriminate stillbirth before and during delivery and rank the features. A two-step stack ensemble classifier is proposed for classifying the instances into stillbirth and livebirth at the first step and then, classifying stillbirth before delivery from stillbirth during the labor at the second step. The proposed SE has two consecutive layers including the same classifiers. The base classifiers in each layer are decision tree, Gradient boosting classifier, logistics regression, random forest and support vector machines which are trained independently and aggregated ba
Data-driven healthcare uses predictive analytics to enhance decision-making and personalized healthcare. Developing prognostic models is one of the applications of predictive analytics in medical environments. Various studies have used machine learning techniques for this purpose. However, there is no specific standard for choosing prediction models for different medical purposes. In this paper, the ISAF framework was proposed for choosing appropriate prediction models with regard to the properties of the classification methods. As one of the case study applications, a prognostic model for predicting cardiac arrests in sepsis patients was developed step by step through the ISAF framework. Finally, a new modified stacking model produced the
Appointment scheduling systems are applied in a broad variety of healthcare environments to reduce costs and increase quality of services. This study is concerned with the problem of appointment scheduling in a distributed multi-hospital network of echocardiography departments. In this paper, a centralized master schedule is presented to maximize profit margin through maximizing the number of performed echoes and minimizing overtime. Developing such a schedule requires handling shift scheduling and capacity allocation problems simultaneously. Based on real-world settings, a mixed integer linear programming model is proposed for the research problem. Since this model requires a large amount of time and memory to provide good solutions, and f
BackgroundGlobalization allows the effects of disruptions to cascade in the systems rapidly and a small disruption could lead to a broad catastrophe. Nowadays, disruptions are becoming more unpredictable, more frequent and more damaging. Hospitals are critical facilities which play an important role after disruptions. Number of deaths and injuries from disasters depends heavily on how hospitals serve people. Therefore, assuring the proper performance of hospitals under disruption is an important issue. Our approach to improve hospital performance is modelling the performance from resilience engineering perspective.MethodsFirst step towards building or designing a resilient organization is assessment and based on that a set of strategies or
BackgroundA significant portion of each country's budget is assigned to its health care system annually. Given the current economic conditions, saving costs is a key issue. Therefore, any situation that leads to lower efficiency and higher costs should be remedied. With much of the hospitals' budgets spent on operating rooms, it is important to use strategies to increase their efficiency and quality. The purpose of this study is to design a mechanism to reduce the cancellation of elective surgical procedures, which incurs considerable costs.MethodsTo this end, the data gathered from an educational hospital in Tehran were evaluated. The reasons for the surgical cancellations were determined through data analysis and process identification. S
English In every Health Transformation Plan, operational challenges and the distance from the primary goals need to be identified. So, the main purpose of this study was obtaining effects and challenges of plan observed by healthcare providers. Participants of this conventional quali? tative method of content analysis study were 45 supervisors and employees who affiliated to Tehran University of Medical Sciences. The challenges differed according to the study places (Community Health Centers, Health Posts and Health Homes) in Work contracts, Integrated Health System, Financial Management, Intermediary companies, Quality, Supervision, Payments, Locations and Family Physician Plan. To effectively implement a health plan and achieve its goals,
Although arteriovenous fistula is the preferred vascular access method, it has challenges in three phases of planning, maturation, and maintenance. We looked at the root of fistula challenges in the maintenance phase and found traces of inflammation. We investigated the role of systemic inflammation in the maintenance phase to understand its effects on post-maturation function and extract knowledge to help extend fistula longevity. Previous studies on fistula longevity have focused entirely on statistical tests. Since these tests put limitations on data, we also used a data mining framework for data analysis. For predictive analysis, we used the decision tree, random forest, and support vector machines. For inferential analysis, we used the
Background: Quality of Intensive care has got more attention in case of the high cost of healthcare and the potential for harm. Poor-quality care causes high cost and quality improvement initiatives in the ICU lead to an improvement in outcomes as well as a decrease in costs. One of the crucial tools that allow physicians and nurses to monitor change in a quality improvement effort is the development of an electronic database for data collection and reporting. The objective of Intensive Care Registries is to create a high-quality registry of patients through a collaboration of academic health centers performing uniform data collection with the purpose of improving the quality and accuracy of healthcare decisions and provide a data-driven cl
Growing demand for medical services has increased patient waiting time due to the limited number or unbalanced distribution of healthcare centers. Healthcare teleconsultation networks are one of the potentially powerful systems to overcome this problem. Medical pathology can hugely benefit from teleconsultation networks because having second opinions is precious for many cases; however, resource planning (i.e., assignment and distribution of pathology consultation requests) is challenging due to bulky medical images of patients. This results in high setup and operational costs. The aim of this study is to design an optimal teleconsultation network for pathology labs under the supervision of medical sciences universities in Tehran, Iran. To
Background and objectives: The regionalization is a suitable approach to reduce the cost of health services and to increase the number of patients covered by special services. Since the establishment of the Neonatal Intensive Care Unit (NICU) needs expensive equipment and experts, it is critical to find the optimal number and location for NICU beds and referral networks. Methods: The geographical access to NICU beds was investigated by collecting the annual demand and the distance between cities at first. The demand consisted of the number of neonates that were born under 32 weeks of gestational age or having less than 1500 gram birth weight in one province of Iran. Next, the location of the available hospital has defined on the map. A maxi
Materials and Methods: Based on this objective, we collected the data of 96 Asthma patients within a 9-month period from a specialized hospital for pulmonary diseases in Tehran. Then we classified the Asthma control level by fuzzy clustering and different types of data mining method within a multivariate dataset with the multi-class response variable.Results: Our best model resulting from the balancing operations and feature selection on data have yielded the accuracy of 88%.Conclusion: Our proposed model can be applied in electronic Asthma self-care systems to support the decision in real time and personalized warnings on the possible deterioration of Asthma control. Such tools can centralize the Asthma treatment from the current reactive
Background: Cardiovascular diseases (CVDs) have always been considered by healthcare 2 specialists for different reasons, including extensive prevalence, high cost, chronicity, and high risk of 3 death. the recovery from CVDs is highly influenced by the behavior and lifestyle. As a result, it seems 4 necessary to train and develop special abilities for patients and their companions, the development of 5 efficient and effective training systems should be considered by healthcare specialists. 6Methods: Hence, in this study, an existing training system for cardiovascular patients is reviewed, 7 and using field observation and targeted interviews with hospital experts, all aspects of its training process, 8 including involved components, inputs