The safety and public health during nuclear power plant operation can be enhanced by accurately recognizing and diagnosing potential problems when a malfunction occurs. However, there are still obvious technological gaps in fault diagnosis applications, mainly because adopting a single fault diagnosis method may reduce fault diagnosis accuracy. In addition, some of the proposed solutions rely heavily on fault examples, which cannot fully cover future possible fault modes in nuclear plant operation. This paper presents the results of a research in hybrid fault diagnosis techniques that utilizes support vector machine (SVM) and improved particle swarm optimization (PSO) to perform further diagnosis on the basis of qualitative reasoning by knowledge-based preliminary diagnosis and sample data provided by an on-line simulation model. Further, SVM has relatively good classification ability with small samples compared to other machine learning methodologies. However, there are some challenges in the selection of hyper-parameters in SVM that warrants the adoption of intelligent optimization algorithms. Hence, the major contribution of this paper is to propose a hybrid fault diagnosis method with a comprehensive and reasonable design. Also, improved PSO combined with a variety of search strategies are achieved and compared with other current optimization algorithms. Simulation tests are used to verify the accuracy and interpretability of research findings presented in this paper, which would be beneficial for intelligent execution of nuclear power plant operation.

A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants.

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

The recent COVID-19 pandemic has affected the world and has the potential to disproportionately affect and disrupt the lives of cancer survivors, including those currently in treatment, those who have completed treatment, and those who are now living cancer-free. There are currently over 17 million cancer survivors in the USA [1] and millions more around the world [2, 3]. Much has been published over the past several decades about the late and long-term effects of cancer treatment, alongside both the challenges and potential solutions to help patients navigate the healthcare system in order to receive high-quality survivorship care [4, 5]. To date, a number of organizations have provided the cancer survivorship community (both patients and healthcare providers) recommendations pertaining to COVID-19 (Box 1). Unfortunately, at this time, there is limited evidence regarding the impact of COVID-19 on cancer survivors, particularly those who have completed treatment. As the pandemic continues to evolve and scientific evidence emerges, more directed recommendations and guidelines will follow. As editors of the Journal of Cancer Survivorship, the only international peer-reviewed publication dedicated to expanding and disseminating knowledge pertaining directly to this patient population, we wrote this commentary to describe how COVID-19 may impact the physical, psychosocial, and healthcare delivery concerns of cancer survivors. We hope that this information may be helpful in addressing the needs of cancer survivors at the present time and frame the issues that will warrant attention in the future.

/pdf/addressing-the-needs-of-cancer-survivors-during-the-covid-19-3l7q8mlll8.pdf

Addressing the needs of cancer survivors during the COVID-19 pandemic.

Liver Disease (LD) is the main cause of death worldwide, affecting a large number of people. A variety of factors affect the liver, resulting in this disease. The diagnosis of this condition is both expensive and time-consuming. Machine Learning offers a lot of potential in terms of automated disease diagnosis. As a result, the purpose of this research is to assess the efficacy of various Machine Learning (ML) algorithms to lower the high cost of liver disease diagnosis through prediction. With the current rise in numerous liver disorders, it’s more important than ever to detect liver disease early on. This research proposed intelligent model to predict liver disease using machine learning technique. This proposed model is more effective and comprehensive in terms of performance of 0.884 accuracy, and 0.116 miss-rate.

Intelligent Model to Predict Early Liver Disease using Machine Learning Technique

To strengthen healthcare industry, lot of research work is being done to explore new, accurate and efficient technology for healthcare industry that can monitor and keep track of health of patients in everyday life or in case of accidents and emergencies. Internet of Things based distributed healthcare system interconnects various medical resources using IoT, distributed databases and distributed DBMSs to provide reliable, effective and smart healthcare service to the elderly and patients with a chronic illness. Internet of Things based distributed healthcare system (IoT-DHS) is a technology that has made major impact on healthcare industry but still there are many challenges and open issues that need to be addressed and find solutions. In IoT-DHS, all available medical resources are interconnected to provide effective and efficient healthcare services to everyone, who is in need of medical assistance. This paper reviews state-of-the-art works in IoT-DHSs, based on which the taxonomy of these systems is proposed considering various aspects such as monitoring methods, communication technologies, computing techniques and low-power protocols. A comparative study of these IoT-DHSs is made to know the suitability of various healthcare systems. The challenges and open issues associated with existing IoT-DHSs are also discussed in detail. This paper helps technical people of medical industry, researchers and scientists interested in cloud computing and IoT technologies for medical field.

Internet of things based distributed healthcare systems: a review

Electronic Health (eHealth) may have a positive effect on healthcare, such as patient education and decreasing the costs of healthcare services. Evidence suggests that such interventions can also improve physical activity (PA) of patients. This systematic review aimed to investigate the effects of PA interventions provided through eHealth on breast cancer patients. This study was conducted through a search in electronic databases up to July 2018. PubMed, EMBASE, Cochrane Central Register of Controlled Trials, Web of Science, Scopus, Science Direct, and Google Scholar databases were searched without time limitation. In total, 2187 articles were retrieved and finally 16 articles remained. Five were pre/post and 11 were randomized trial studies. Different platforms were used in these studies including web-based, mobile-based, both web-and-mobile-based and email. In total, these articles comprise 2304 breast cancer patients with the mean age of 51 years and 50% were conducted in the USA. Four studies measured PA using wearable devices such as accelerometers and pedometers. All studies reported an increase in PA level at least in one of moderate or vigorous PA, although not all these results were significant. The results show that eHealth interventions can improve the level of PA in breast cancer patients. Although there are numerous eHealth interventions focusing on PA in cancer patients, there is still an essential need for eHealth interventions to be tailored for breast cancer patients specifically. Clinical trials with appropriate methodology, enough intervention time and follow-up are needed to make evidence-based results more generalizable. PROSPERO CRD42018092422; https://www.crd.york.ac.uk/PROSPERO/.

A Systematic Review of Electronic Health (eHealth) interventions to improve physical activity in patients with breast cancer.

Objective:Bone is the most common site of metastasis in breast cancer. Prognostic factors for predicting bone metastases in breast cancer are controversial yet. In this study, we investigated clini...

https://journals.sagepub.com/doi/pdf/10.1177/1178223419830978

Bone Metastasis Prognostic Factors in Breast Cancer

Introduction: The Adaptive neuro-fuzzy inference system (ANFIS) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. In this study, this model was used for breast

/pdf/breast-cancer-risk-assessment-using-adaptive-neuro-fuzzy-4v5lyuv6bv.pdf

Breast Cancer Risk Assessment Using adaptive neuro-fuzzy inference system (ANFIS) and Subtractive Clustering Algorithm

Background: Timely diagnosis of liver cirrhosis is vital for preventing further liver damage and giving the patient the chance of transplantation. Although biopsy of the liver is the gold standard for cirrhosis assessment, it has some risks and limitations and this has led to the development of new noninvasive methods to determine the stage and prognosis of the patients. We aimed to design an artificial neural network (ANN) model to diagnose cirrhosis patients with non-alcoholic fatty liver disease (NAFLD) using routine laboratory data. 

Methods: Data were collected from 392 patients with NAFLD by the Middle East Research Center in Tehran. Demographic variables, history of diabetes, INR, complete blood count, albumin, ALT, AST and other routine laboratory tests, examinations and medical history were gathered. Relevant variables were selected by means of feature extraction algorithm (Knime software) and were accredited by the experts. A neural network was developed using the MATLAB software. 

Results: The best obtained model was developed with two layers, eight neurons and TANSIG and PURLIN functions for layer one and output layer, respectively. The sensitivity and specificity of the model were 86.6% and 92.7%, respectively. 

Conclusion: The results of this study revealed that the neural network modeling may be able to provide a simple, noninvasive and accurate method for diagnosing cirrhosis only based on routine laboratory data.

http://mjiri.iums.ac.ir/files/site1/user_files_e9487e/admin-A-10-1-743-008bc52.pdf

A diagnostic model for cirrhosis in patients with non-alcoholic fatty liver disease: an artificial neural network approach

Introduction:Lymphedema is one of the complications of breast cancer treatment. It has no cure yet and can affect the quality of life. This study aimed to identify and investigate informational nee...

https://journals.sagepub.com/doi/pdf/10.1177/1178223420911033

Sara Dorri

Papers

A Systematic Review of Electronic Health (eHealth) interventions to improve physical activity in patients with breast cancer.

Bone Metastasis Prognostic Factors in Breast Cancer

Breast Cancer Risk Assessment Using adaptive neuro-fuzzy inference system (ANFIS) and Subtractive Clustering Algorithm

A diagnostic model for cirrhosis in patients with non-alcoholic fatty liver disease: an artificial neural network approach

Informational Needs in Patients With Breast Cancer With Lymphedema: Is It Important?