An intelligent content discovery technique for health portal content management

Background: Continuous content management of health information portals is a feature vital for its sustainability and widespread acceptance. Knowledge and experience of a domain expert is essential for content management in the health domain. The rate of generation of online health resources is exponential and thereby manual examination for relevance to a specific topic and audience is a formidable challenge for domain experts. Intelligent content discovery for effective content management is a less researched topic. An existing expert-endorsed content repository can provide the necessary leverage to automatically identify relevant resources and evaluate qualitative metrics.Objective: This paper reports on the design research towards an intelligent technique for automated content discovery and ranking for health information portals. The proposed technique aims to improve efficiency of the current mostly manual process of portal content management by utilising an existing expert-endorsed content repository as a supporting base and a benchmark to evaluate the suitability of new content.Methods: A model for content management was established based on a field study of potential users. The proposed technique is integral to this content management model and executes in several phases (ie, query construction, content search, text analytics and fuzzy multi-criteria ranking). The construction of multi-dimensional search queries with input from Wordnet, the use of multi-word and single-word terms as representative semantics for text analytics and the use of fuzzy multi-criteria ranking for subjective evaluation of quality metrics are original contributions reported in this paper.Results: The feasibility of the proposed technique was examined with experiments conducted on an actual health information portal, the BCKOnline portal. Both intermediary and final results generated by the technique are presented in the paper and these help to establish benefits of the technique and its contribution towards effective content management.Conclusions: The prevalence of large numbers of online health resources is a key obstacle for domain experts involved in content management of health information portals and websites. The proposed technique has proven successful at search and identification of resources and the measurement of their relevance. It can be used to support the domain expert in content management and thereby ensure the health portal is up-to-date and current

Development of User Warrant Ontology for Improving Online Health Information Provision

Health information portals (HIP) are gateways to reliable and personalised online health information. In practice, however, searching for information in HIP is still far from being effective due to the intricate nature of health information provision. Previous studies have shown the emerging trend of using domain ontology to address the retrieval issue in online healthcare information. Yet, the suitability of domain ontology alone for HIPs is still questionable due to the varied levels of user behaviour and preferences in information search. Inspired by this problem, we propose an ontology development method grounded on the collaboration between user warrant principles, knowledge engineering, and design science framework. The paper reports the development method and the implementation of such an user-warrant ontology that accommodates user-sensitivity into HIP. The evaluation process is conducted by domain experts responsible for portal management and validates the external semantic of the ontology according to a set of pre-defined evaluation criteria. Results from the application of this methodology to an actual HIP are also reported as this research demonstrates the potential of user warrant ontology to resolve information retrieval problem in HIP

Supporting personalised content management in smart health information portals

Information portals are seen as an appropriate platform for personalised healthcare and wellbeing information provision. Efficient content management is a core capability of a successful smart health information portal (SHIP) and domain expertise is a vital input to content management when it comes to matching user profiles with the appropriate resources. The rate of generation of new health-related content far exceeds the numbers that can be manually examined by domain experts for relevance to a specific topic and audience. In this paper we investigate automated content discovery as a plausible solution to this shortcoming that capitalises on the existing database of expert-endorsed content as an implicit store of knowledge to guide such a solution. We propose a novel content discovery technique based on a text analytics approach that utilises an existing content repository to acquire new and relevant content. We also highlight the contribution of this technique towards realisation of smart content management for SHIPs.<br /

A Participatory Information Management Framework for Patient Centred Care of Autism Spectrum Disorder

Patient-centred care (PCC) is grounded on the relationships formed between healthcare professionals, patients and patients’ family members. This network of stakeholders is frequently found to be disconnected due to the absence of an enabling framework. Active online participation and continuous engagement improves patients’ healthcare experience and healthcare professionals’ understanding of the illness. The community setting of PCC further generates crowd intelligence which in turn complements the knowledge of clinical experts. This body of evolving knowledge is a valuable resource with long term impact for both current and new patients as well as healthcare professionals. It is highly relevant for spectrum illnesses that usually span across the lifetime of a patient, such as Autism Spectrum Disorder (ASD). A framework provides structure to such a body of knowledge and defines functionality that delivers and sustains its use. This paper presents a participatory information management (PIM) framework for the delivery of PCC for ASD in a health, education and community service setting. The framework is founded on the updated IS participation theory. Driven by patient participation, it expands thereon to intersect community and clinician participation. As discussed in the paper, the potential outcomes are broad, ranging from improved healthcare quality to enabling translational research. An ongoing pilot project applying the framework to ASD is also reported in the paper

Addressing the complexities of big data analytics in healthcare : The diabetes screening case

The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context. © 2015 De Silva, Burstein, Jelinek, Stranieri

An Artificial Intelligence Framework for Bidding Optimization with Uncertainty inMultiple Frequency Reserve Markets

The global ambitions of a carbon-neutral society necessitate a stable and robust smart grid that capitalises on frequency reserves of renewable energy. Frequency reserves are resources that adjust power production or consumption in real time to react to a power grid frequency deviation. Revenue generation motivates the availability of these resources for managing such deviations. However, limited research has been conducted on data-driven decisions and optimal bidding strategies for trading such capacities in multiple frequency reserves markets. We address this limitation by making the following research contributions. Firstly, a generalised model is designed based on an extensive study of critical characteristics of global frequency reserves markets. Secondly, three bidding strategies are proposed, based on this market model, to capitalise on price peaks in multi-stage markets. Two strategies are proposed for non-reschedulable loads, in which case the bidding strategy aims to select the market with the highest anticipated price, and the third bidding strategy focuses on rescheduling loads to hours on which highest reserve market prices are anticipated. The third research contribution is an Artificial Intelligence (AI) based bidding optimization framework that implements these three strategies, with novel uncertainty metrics that supplement data-driven price prediction. Finally, the framework is evaluated empirically using a case study of multiple frequency reserves markets in Finland. The results from this evaluation confirm the effectiveness of the proposed bidding strategies and the AI-based bidding optimization framework in terms of cumulative revenue generation, leading to an increased availability of frequency reserves

Multivariate data-driven decision guidance for clinical scientists

Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards utilising better information management for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges created for effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. A Data-driven Decision Guidance Management System (DD-DGMS) architecture can encompass solutions into a single closed-loop integrated platform to empower clinical scientists to seamlessly explore a multivariate data space in search of novel patterns and correlations to inform their research and practice. The paper describes the components of such an architecture, which includes a robust data warehouse as an infrastructure for comprehensive clinical knowledge management. The proposed DD-DGMS architecture incorporates the dynamic dimensional data model as its elemental core. Given the heterogeneous nature of clinical contexts and corresponding data, the dimensional data model presents itself as an adaptive model that facilitates knowledge discovery, distribution and application, which is essential for clinical decision support. The paper reports on a trial of the DD-DGMS system prototype conducted on diabetes screening data which further establishes the relevance of the proposed architecture to a clinical context.E

Kidney Tumor Detection using Attention based U-Net

The advancement of deep learning techniques has provoked the potential of using Medical Image Analysis (MIA) for disease detection and prediction in numerous ways. This has been mostly useful in identifying tumours and abnormalities in many organs of the human body. Particularly in kidney diseases, the treatment options such as surgery have largely benefitted by the ability to detect tumours in early stages, thereby shifting towards more efficient methods including conservative nephron procedures. Therefore, to enable the early detection of kidney tumours, we propose a convolutional neural network based U-Net architecture which is able to detect tumours using an attention mechanism. The proposed architecture was evaluated using KiTS19 Challenge dataset that includes a collection of multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes for 300 patients who underwent nephrectomy for kidney tumours. The outcomes demonstrate the ability of the proposed architecture to distinguish images with tumours in the kidney and support early tumour detection

Incidence of Bladder Cancer in Sri Lanka: Analysis of the Cancer Registry Data and Review of the Incidence of Bladder Cancer in the South Asian Population

PurposeTo investigate the incidence of bladder cancer (BC) in Sri Lanka and to compare risk factors and outcomes with those of other South Asian nations and South Asian migrants to the United Kingdom (UK) and the United States (US).Materials and MethodsThe incidence of BC in Sri Lanka was examined by using two separate cancer registry databases over a 5-year period. Smoking rates were compiled by using a population-based survey from 2001 to 2009 and the relative risk was calculated by using published data.ResultsA total of 637 new cases of BC were diagnosed over the 5-year period. Sri Lankan BC incidence increased from 1985 but remained low (1.36 and 0.3 per 100,000 in males and females) and was similar to the incidence in other South Asian countries. The incidence was lower, however, than in migrant populations in the US and the UK. In densely populated districts of Sri Lanka, these rates almost doubled. Urothelial carcinoma accounted for 72%. The prevalence of male smokers in Sri Lanka was 39%, whereas Pakistan had higher smoking rates with a 6-fold increase in BC.ConclusionsSri Lankan BC incidence was low, similar to other South Asian countries (apart from Pakistan), but the actual incidence is likely higher than the cancer registry rates. Smoking is likely to be the main risk factor for BC. Possible under-reporting in rural areas could account for the low rates of BC in Sri Lanka. Any genetic or environmental protective effects of BC in South Asians seem to be lost on migration to the UK or the US and with higher levels of smoking, as seen in Pakistan