Farid Shirvani

Bio: Farid Shirvani is an academic researcher from University of Wollongong. The author has contributed to research in topics: System of systems & Procurement. The author has an hindex of 4, co-authored 19 publications receiving 47 citations.

Applying a novel combination of techniques to develop a predictive model for diabetes complications.

Abstract: Among the many related issues of diabetes management, its complications constitute the main part of the heavy burden of this disease. The aim of this paper is to develop a risk advisor model to predict the chances of diabetes complications according to the changes in risk factors. As the starting point, an inclusive list of (k) diabetes complications and (n) their correlated predisposing factors are derived from the existing endocrinology text books. A type of data meta-analysis has been done to extract and combine the numeric value of the relationships between these two. The whole n (risk factors) - k (complications) model was broken down into k different (n-1) relationships and these (n-1) dependencies were broken into n (1-1) models. Applying regression analysis (seven patterns) and artificial neural networks (ANN), we created models to show the (1-1) correspondence between factors and complications. Then all 1-1 models related to an individual complication were integrated using the naive Bayes theorem. Finally, a Bayesian belief network was developed to show the influence of all risk factors and complications on each other. We assessed the predictive power of the 1-1 models by R2, F-ratio and adjusted R2 equations; sensitivity, specificity and positive predictive value were calculated to evaluate the final model using real patient data. The results suggest that the best fitted regression models outperform the predictive ability of an ANN model, as well as six other regression patterns for all 1-1 models.

Enhancement of FMEA risk assessment with SysML

Abstract: Assuring the safety of workers and passengers is a primary concern that has to be addressed in transport systems such as Heavy Rail. Introduction of new technologies into existing operation...

Employing the model based systems engineering methodologies to develop a domain specific language for contracting of infrastructure projects

Abstract: The procurement of infrastructure systems by the public sector is very costly, long and not transparent since the processes are based on preparing huge amounts of documents which are difficult to be kept consistent and to be used (e.g. bid evaluation). Acquisition architecture frameworks (AF) are metamodels, developed to model the whole enterprise/system life cycle stages including system procurement. Our previous study analyzed the currently used AFs such as DoDAF, MoDAF and TRAK to assess their adequacy and efficiency in modelling the infrastructure projects. The results showed that many of the procurement related concerns are overlooked such as financial matters e.g. cost and revenue calculation; and risk management aspects e.g. risk calculation and risk allocation. This paper focuses on identifying the procurement concerns and adding new viewpoints to the architecture frameworks; and developing a domain specific language based on SysML to model the new viewpoints. A methodology is provided which shows how the metamodel (abstract syntax) and the language stereotypes (concrete syntax) are developed. The results firstly show the 18 identified viewpoints of procurement domain and then one of them (project funding) is chosen to be described in this paper. The conceptual definition of the ‘project funding’ viewpoint and the models it generates are illustrated as example diagrams of this DSL. This DSL can be used by the domain practitioners, who are the contracting officers and procurement managers, to generate the contracting materials to facilitate the contracting process, assure the consistency of the procurement documents, giving better project outcomes.

Developing a Modelling Framework for Aligning the Human Aspects to the Physical System in Large Complex Systems

Abstract: Rail organizations are very complex systems involving both the physical aspects of the rail system and the human aspects of the operating organization. Introduction of new technologies to the physical system affects the human aspects, so rail managers are concerned with maintaining the integrity across the whole system as it evolves. This paper describes a modelling framework based on the ISO42010 standard which allows for generating consistent models that help with overcoming the challenges raised by interconnectivity of the physical system and organization components. The foundation of the framework is a metamodel (an abstract model of the whole domain) which can be instantiated to various real organizations. The interconnected elements of the metamodel holds the relationships between the model elements which are mapped to the domain elements, so the interconnectivity among the rail system components and the human related requirements can be traced and maintained.

Infrastructure system of systems integrity

Abstract: Infrastructure systems typically consist of technical structures comprising of physical and operational components of interrelated constituent systems forming what is now known as system of systems (SOS). The complexity and uncertainty of unforeseen events that are inherent characteristic of infrastructure systems makes it impossible to predict undesirable emergent behaviours that could push the operation of such systems away from their intended purposes. Infrastructure systems present numerous challenges throughout their lifecycles. This paper addresses one of these challenges that is presented during operation, when managers need to report 'how well' the system is performing and find ways to address the consequences of unexpected events that often degrade the intended performance. This paper adopts a definition of system integrity (SI) to assess the SI for each constituent system and then combines them into the overall SI for the SoS. The proposed method is based on the on-going operational performance, safety and resilience of the constituent systems and applies the Analytic Hierarchy Process (AHP) to create a quantitative value derived from experience-based qualitative assessment. In this method, firstly the key performance indicators (KPI) for each of the agreed assessment criteria for operational performance, safety and resilience are defined and individually assessed. Then the KPIs for each of the three criteria are weighted relatively to each other to obtain the overall assessment for operational performance, safety and resilience for each individual system. These three criteria are also compared and weighted to determine their level of contribution to the SI for the system which is then calculated. The method is then expanded to assess SI for SoS and applied into a hypothetical urban transport system for illustration purposes.

Harrison's Principles of Internal Medicine

Abstract: The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Applications of artificial neural networks in health care organizational decision-making: A scoping review

Abstract: Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. Applications of ANN to diagnosis are well-known; however, ANN are increasingly used to inform health care management decisions. We provide a seminal review of the applications of ANN to health care organizational decision-making. We screened 3,397 articles from six databases with coverage of Health Administration, Computer Science and Business Administration. We extracted study characteristics, aim, methodology and context (including level of analysis) from 80 articles meeting inclusion criteria. Articles were published from 1997–2018 and originated from 24 countries, with a plurality of papers (26 articles) published by authors from the United States. Types of ANN used included ANN (36 articles), feed-forward networks (25 articles), or hybrid models (23 articles); reported accuracy varied from 50% to 100%. The majority of ANN informed decision-making at the micro level (61 articles), between patients and health care providers. Fewer ANN were deployed for intra-organizational (meso- level, 29 articles) and system, policy or inter-organizational (macro- level, 10 articles) decision-making. Our review identifies key characteristics and drivers for market uptake of ANN for health care organizational decision-making to guide further adoption of this technique.

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Abstract: Background: Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis. Objective: The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges. Methods: A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review. Results: We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results. Conclusions: We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.

Failure Mode and Effects Analysis (FMEA)

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