8T SRAM, using domino read, is preferred for small-size and high-performance arrays [1]. Ripple domino circuitry relies on rail-to-rail readout, which forces a trade off between performance and the number of 8T cells on a local read bit line (RBL). To support larger array sizes, without sacrificing performance, arrays are segmented, and several segments are stitched to form the full array. Each segment requires local evaluation circuitry to connect local BL to global BLs. This local circuitry, along with the required layout fencing of each segment results in poor array efficiency.

8T SRAM, using domino read, is preferred for small-size and high-performance arrays [1]. Ripple domino circuitry relies on rail-to-rail readout, which forces a trade off between performance and the number of 8T cells on a local read bit line (RBL). To support larger array sizes, without sacrificing performance, arrays are segmented, and several segments are stitched to form the full array. Each segment requires local evaluation circuitry to connect local BL to global BLs. This local circuitry, along with the required layout fencing of each segment results in poor array efficiency.

In order to manage large collections of video content, we need appropriate video content models that can facilitate interaction with the content. The important issue for video applications is to accommodate different ways in which a video sequence can function semantically. This requires that the content be described at several levels of abstraction. In this chapter we propose a video metamodel called VIMET and describe an approach to modeling video content such that video content descriptions can be developed incrementally, depending on the application and video genre. We further define a data model to represent video objects and their relationships at several levels of abstraction. With the help of an example, we then illustrate the process of developing a specific application model that develops incremental descriptions of video semantics using our proposed video metamodel (VIMET).

Polypharmacy or concurrent intake of multiple medications is often associated with negative health outcomes and adverse drug reactions. Routinely collected administrative health data can be a potential and inexpensive alternative to study large population to understand this polypharmacy phenomenon and associated risk. However, synthesizing medication intakes from pharmaceutical records of administrative data can be challenging. In this study, we proposed a graph or network-based approach to understand polypharmacy utilizing the 10% Pharmaceutical and Medicare Benefits Scheme sample data in Australian healthcare context. We proposed methods to identify drug regimens from discrete information of drug dispenses. A polymedication network is then generated from the regimens. We also explored potential relationship among patients' age, medical and pharmaceutical costs and several categories of polymedication regimens. The result showed complex relationships among various drugs and signified the multimorbidity nature of the targeted treatments. Especially the long-term polymedication regimens are found to be focused on treating chronic conditions like cardiovascular diseases, diabetes, asthma, COPD and acid reflux, consistent with the Australian population's disease burden. The methods and networked approach presented in this study can act as a basis for further pharmacovigilance and identifying adverse drug reactions.

This paper is a case study of diagnostic techniques used to debug a particularly difficult fail in a multi-port register file memory that appeared to increase its minimum functional voltage (VMIN) over time. Some of the debug techniques used involved Array Built-In-Self Test (ABIST) before and after chips in burn in, CPA (Critical Parameters Analysis), PEM (Photon Emission Microscopy), PICA (Picosecond Image Circuit Analysis) and PFA (Physical Failure Analysis).

Very high adoption of mobile phones enables the possibility of using mobile phones to empower type 2 diabetes patients to self-manage their diabetic condition by providing timely information in right context through the mobile application, thus facilitating them to make informed decisions. Having identified the lack of such information is badly affecting type 2 patients we embarked on a project to develop a mobile-based information system. Iterative development of interfaces for the mobile application was carried out and its relevance evaluated.

Clinical diagnosis and regular monitoring of the population at risk of chronic diseases is clinically and financially resource-intensive. Mining administrative data could be an effective alternative way to identify this high-risk cohort. In this research, we apply data mining and network analysis technique on hospital admission and discharge data to understand the disease or comorbidity footprints of chronic patients. Based on this understanding we have developed a chronic disease risk prediction framework. The framework is then tested on Australian healthcare context to predict type 2 diabetes (T2D) risk. The dataset contained approximately 1.4 million admission records from 0.75 million patients. From this, we filtered and sampled the records of 2300 patients having comorbidities including T2D and another 2300 patients having comorbidities other than T2D. Along with demographic and behavioral risk factors for prediction, we propose several graph theory and social network-based measures which indicate the prevalence of comorbidities, transition patterns, and clustering membership. We use an exploratory approach to understand the relative impact of these risk factors and evaluate the prediction performance using three different predictive methods-regression, parameter optimization, and tree classification. All three prediction methods gave the highest ranking to the graph theory-based 'comorbidity prevalence' and 'transition pattern match' scores showing the effectiveness of the proposed network theory-based measures. Overall, the prediction accuracy between 82% to 87% shows the potential of the framework utilizing administrative data. The proposed framework could be useful for governments and health insurers to identify high-risk chronic disease cohorts. Developing preventive strategies then, over a period of time, can reduce the burden of acute care hospitalization. (C) 2019 Elsevier Ltd. All rights reserved.

Very high adoption of mobile phones enables the possibility of using mobile phones to empower type 2 diabetes patients to self-manage their diabetic condition by providing timely information in right context through the mobile application, thus facilitating them to make informed decisions. Having identified the lack of such information is badly affecting type 2 patients we embarked on a project to develop a mobile-based information system. We have developed an architecture for a mobile application that can help in empowering users to self-manage type 2 diabetes. We have received positive feedbacks from patients and health care provider after carrying out the evaluation of the application.

Methods, apparatus, systems and articles of manufacture to compiler compile code to generate dataflow code are described. An example compiler apparatus includes an intermediate representation transformer to transform input software code to intermediate representation code; an instruction selector to insert machine instructions of a target execution platform in the intermediate representation code to generate machine intermediate representation code; and a target machine transformer to: convert a portion of the machine intermediate representation code to dataflow code to generate dataflow intermediate representation code; and allocate registers within the dataflow intermediate representation code.

BACKGROUND:Chronic diseases management outside expensive hospital settings has become a major target for governments, funders and healthcare service providers. It is well known that chronic diseases such as Type 2 Diabetes (T2D) do not occur in isolation, and has a shared aetiology common to many other diseases and disorders. Diabetes Australia reports that it is associated with a myriad of complications, which affect the feet, eyes, kidneys, and cardiovascular health. For instance, nerve damage in the lower limbs affects around 13% of Australians with diabetes, diabetic retinopathy occurs in over 15% of Australians with diabetes, and diabetes is now the leading cause of end-stage kidney disease. Our research focus is therefore to understand the comorbidity pattern, which in turn can enhance our understanding of the multifactorial risk factors of chronic diseases like Type 2 Diabetes. Our research approach is based on utilising valuable indicators present in pre-existing administrative healthcare data, which are routinely collected but often neglected in health research. One such administrative healthcare data is the hospital admission and discharge data that carries information about diagnoses, which are represented in the form of ICD-10 diagnosis codes. Analysis of diagnoses codes and their relationships helps us construct comorbidity networks which can provide insights that can be used to understand chronic disease progression pattern and comorbidity network at a population level. This understanding can subsequently enable healthcare providers to formulate appropriate preventive health policies targeted to address high-risk chronic conditions.METHODS AND FINDINGS:The research utilises network theory principles applied to administrative healthcare data. Given the high rate of prevalence, we selected Type 2 Diabetes as the exemplar chronic disease. We have developed a research framework to understand and represent the progression of Type 2 diabetes, utilising graph theory and social network analysis techniques. We propose the concept of a 'comorbidity network' that can effectively model chronic disease comorbidities and their transition patterns, thereby representing the chronic disease progression. We further take the attribution effect of the comorbidities into account while generating the network; that is, we not only look at the pattern of disease in chronic disease patients, but also compare the disease pattern with that of non-chronic patients, to understand which comorbidities have a higher influence on the chronic disease pathway. The research framework enables us to construct a baseline comorbidity network for each of the two cohorts. It then compares and merges these two networks into single comorbidity network to discover the comorbidities that are exclusive to diabetic patients. This framework was applied on administrative data drawn from the Australian healthcare context. The overall dataset contained approximately 1.4 million admission records from 0.75 million patients, from which we filtered and sampled the records of 2300 diabetics and 2300 non-diabetic patients. We found significant difference in the health trajectory of diabetic and non-diabetic cohorts. The diabetic cohort exhibited more comorbidity prevalence and denser network properties. For example, in the diabetic cohort, heart and liver-related disorders, cataract etc. were more prevalent. Over time, the prevalence of diseases in the health trajectory of diabetic cohorts were almost double of the prevalence in the non-diabetic cohort, indicating entirely different ways of disease progression.CONCLUSIONS:The paper presents a research framework based on network theory to understand chronic disease progression along with associated comorbidities that manifest over time. The analysis methods provide insights that can enable healthcare providers to develop targeted preventive health management programs to reduce hospital admissions and associated high costs. The baseline comorbidity network has the potential to be used as the basis to develop a chronic disease risk prediction model.

The recommender system becomes a significant research area due to the popularity of the social web.Traditional semantic recommender systems deliver poor performance when balancing the recommendation accuracy and diversity.Also, the rank-based recommendation methods lack to obtain the coverage of the entire preferences of the user in the top-N recommendation list.Thus, this paper presents the Diversity-Ensured Semantic-aware Item REcommendation (DESIRE) that deals with the consistent and reliable knowledge source to significantly improve the quality and provide the diversity-ensured top-N recommendation list.The DESIRE approach builds the semantically relevant graphs such as movie-centric and user rating-centric graph with the help of both the Linked Open Data (LOD) and the explicit ratings of the users.By extracting the semantic-path based features from the user rating-centric graph, it executes the ranking algorithm for the top-N movie recommendation.Moreover, the diversity-aware re-ranking tends to maintain the trade-off between the diversity and accuracy in the top-N recommendation.

Hospitals routinely collect admitted patients' data for administrative purposes and for reporting to the government and health insurers. These heterogeneous and mostly untapped data contain rich semantic information about patients' health conditions in the form of standard disease codes. These traces of clinical information can be aggregated over patients to understand how their health progresses over time. When applied on particular chronic disease patients, this approach can potentially help in understanding chronic disease comorbidities as well as in the knowledge discovery on how the chronic disease progresses over time. In this paper, we propose a network-based approach to extract semantic information from hospital administrative data in order to develop a representation of chronic disease progression specifically - type 2 diabetes. We then propose measures for attribution adjustment that ranks the more prevalent comorbidities in chronic patients higher, compared to the non-chronic ones. We also have applied the framework on the administrative data of 2,760 sampled patients to understand how diabetes progresses over time through different comorbidities. This understanding can be effectively converted to actionable intelligence that can be useful in the formulation of better health policy and resource management.

Current high numbers of diabetes patients worldwide and the associated cost indicates current approaches to managing this chronic disease is not effective. The literature indicates the need to provide context specific actionable information to these patients to better manage diabetes which is not happening at present. Making use of wide spread digital connectivity now available due to rapid growth of mobile phone usage and concept of Digital Knowledge Ecosystem to enhance flow of information within a domain we applied scenario based design approach to develop a new scenario for better manage diabetes. The transformed scenario captures the current life situation such as exercise, food and emotional habit of the patient on a day to day basis using mobile application and sensor devices. This collected information is aggregated and provided to the care provider, allowing the care provider to understand which advice has been successfully followed and which of the advices need to be modified to suit the needs of the patient. The care provider can set up modified advice as protocols through the system. The protocols are then used by the system based on the context to generate the set of daily actions that the patient has to perform. We also developed the initial set of user interfaces to support the transformed scenario. Thus we have shown that conceptually now it is possible to have a new scenario to better manage diabetes overcoming deficiencies reported in literature of the current scenario.

A patient-centric approach to healthcare leads to an informal social network among medical professionals. This chapter presents a research framework to: (1) identify the collaboration structure among physicians that is effective and efficient for patients; (2) discover effective structural attributes of a collaboration network that evolves during the course of providing care; and (3) explore the impact of socio-demographic characteristics of healthcare professionals, patients, and hospitals on collaboration structures, from the point of view of measurable outcomes such as cost and quality of care. The framework uses illustrative examples drawn from a data set of patients undergoing hip replacement surgery. The practical application of the proposed framework reveals structures of physicians' collaborations that are not favourable to cost and quality of care measures such as readmission rate. The authors believe that such a framework will enable healthcare managers and administrators to evaluate the collaborative work environment within their respective healthcare organisations.

This chapter provides some basic network-based concepts and analytical measures that can be computed from healthcare data. It describes some healthcare applications, where network analytics can provide insights to healthcare managers for evidence-based decisions. The chapter explains how social network analysis can be used to reveal insights into each of the three stakeholder groups, to make informed decisions that can influence both cost and quality of healthcare from their own perspectives. It classifies the basic social network measures under the following three groups: node-level measures, network-level measures, and measures for subgroup analysis. Network analysis is used to explore administrative data from two perspectives: to explore different aspects of collaboration among physicians, and to understand the health trajectory of chronic disease patients. These applications are explicitly designed to support informed decision making for the three stakeholder groups, namely, the consumer, the provider, and the policy maker.

Previous studies have documented the application of electronic health insurance claim data for health services research purposes. In addition to administrative and billing details of healthcare services, insurance data reveal important information regarding professional interactions and/or links that emerge among healthcare service providers through, for example, informal knowledge sharing. By using details of such professional interactions and social network analysis methods, the aim of the present study was to develop a research framework to explore health care coordination and collaboration. The proposed framework was used to analyse a patient-centric care coordination network and a physician collaboration network. The usefulness of this framework and its applications in exploring collaborative efforts of different healthcare professionals and service providers is discussed.

Governments all over the world are concerned about the disease burden caused by chronic conditions. A significant portion of this comes from potentially preventable hospital admissions. By adopting preventive measures, these admissions can be avoided which in turn can reduce cost and health risk, further benefitting the funders, providers and patients as well. One potential approach can be to look at healthcare information system, more specifically - hospital admission data that carries rich semantic information. In this paper we present a novel framework to apply social network and graph theoretic methods on modern healthcare data to analyse and understand chronic disease progression to enable all stakeholders to take appropriate preventive measures.