Previous Issue

Table of Contents

Informatics, Volume 5, Issue 4 (December 2018)

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
View options order results:
result details:
Displaying articles 1-3
Export citation of selected articles as:
Open AccessDiscussion Theory and Practice in Digital Behaviour Change: A Matrix Framework for the Co-Production of Digital Services That Engage, Empower and Emancipate Marginalised People Living with Complex and Chronic Conditions
Informatics 2018, 5(4), 41; https://doi.org/10.3390/informatics5040041 (registering DOI)
Received: 10 September 2018 / Revised: 31 October 2018 / Accepted: 5 November 2018 / Published: 9 November 2018
PDF Full-text (692 KB) | HTML Full-text | XML Full-text
Abstract
Background: The WHO framework on integrated people-centred health services promotes a focus on the needs of people and their communities to empower them to have a more active role in their own health. It has advocated five strategies including: Engaging and empowering people
[...] Read more.
Background: The WHO framework on integrated people-centred health services promotes a focus on the needs of people and their communities to empower them to have a more active role in their own health. It has advocated five strategies including: Engaging and empowering people and communities; co-ordinating services within and across sectors; and, creating an enabling environment. Any implementation of these strategies needs to occur at individual, community, and health service levels. Useful steps to reorganising health service provision are already being guided by existing models of care linked to increased adoption and use of digital technologies with examples including: Wagner’s Chronic Care Model (CCM); Valentijn’s Rainbow Model of Integrated Care (RMIC); and Phanareth’s et al.’s Epital Care Model (ECM). However, what about individuals and the communities they live in? How will strategies be implemented to address known inequities in: the social determinants of health; access to, and use of digital technologies, and individual textual, technical, and health literacies? Proposal of a matrix framework: This paper argues that people with complex and chronic conditions (PwCCC) living in communities that are at risk of being under-served or marginalised in health service provision require particular attention. It articulates a step-by-step process to identify these individuals and co-produce mechanisms to engage, empower and ultimately emancipate these individuals to become activated in living with their conditions and in their interactions with the health system and community. This step-by-step process focuses on key issues related to the design and role of digital services in mitigating the effects of the health service inequity and avoiding the creation of an e-health divide amongst users when advocating digital behaviour change initiatives. This paper presents a matrix framework providing a scaffold across three inter-related levels of the individual; the provider, and the health and care system. The matrix framework supports examination of and reflection on the design and role of digital technologies in conjunction with pre-existing motivational instruments. This matrix framework is illustrated with examples from practice. Conclusion: It is anticipated that the matrix framework will evolve and can be used to map and reflect on approaches and practices aiming to enrich and stimulate co-production activities supported by digital technology focused on enhancing people-centred health services for the marginalised. Full article
(This article belongs to the Section Digital Humanities)
Figures

Figure 1

Open AccessArticle On Ensemble SSL Algorithms for Credit Scoring Problem
Informatics 2018, 5(4), 40; https://doi.org/10.3390/informatics5040040
Received: 17 September 2018 / Revised: 23 October 2018 / Accepted: 26 October 2018 / Published: 28 October 2018
Viewed by 197 | PDF Full-text (778 KB) | HTML Full-text | XML Full-text
Abstract
Credit scoring is generally recognized as one of the most significant operational research techniques used in banking and finance, aiming to identify whether a credit consumer belongs to either a legitimate or a suspicious customer group. With the vigorous development of the Internet
[...] Read more.
Credit scoring is generally recognized as one of the most significant operational research techniques used in banking and finance, aiming to identify whether a credit consumer belongs to either a legitimate or a suspicious customer group. With the vigorous development of the Internet and the widespread adoption of electronic records, banks and financial institutions have accumulated large repositories of labeled and mostly unlabeled data. Semi-supervised learning constitutes an appropriate machine- learning methodology for extracting useful knowledge from both labeled and unlabeled data. In this work, we evaluate the performance of two ensemble semi-supervised learning algorithms for the credit scoring problem. Our numerical experiments indicate that the proposed algorithms outperform their component semi-supervised learning algorithms, illustrating that reliable and robust prediction models could be developed by the adaptation of ensemble techniques in the semi-supervised learning framework. Full article
Open AccessPerspective Large Scale Advanced Data Analytics on Skin Conditions from Genotype to Phenotype
Informatics 2018, 5(4), 39; https://doi.org/10.3390/informatics5040039
Received: 31 July 2018 / Revised: 26 September 2018 / Accepted: 9 October 2018 / Published: 23 October 2018
Viewed by 243 | PDF Full-text (1004 KB) | HTML Full-text | XML Full-text
Abstract
A crucial factor in Big Data is to take advantage of available data and use that for new discovery or hypothesis generation. In this study, we analyzed Large-scale data from the literature to OMICS, such as the genome, proteome or metabolome, respectively, for
[...] Read more.
A crucial factor in Big Data is to take advantage of available data and use that for new discovery or hypothesis generation. In this study, we analyzed Large-scale data from the literature to OMICS, such as the genome, proteome or metabolome, respectively, for skin conditions. Skin acts as a natural barrier to the world around us and protects our body from different conditions, viruses, and bacteria, and plays a big part in appearance. We have included Hyperpigmentation, Postinflammatory Hyperpigmentation, Melasma, Rosacea, Actinic keratosis, and Pigmentation in this study. These conditions have been selected based on reasoning of big scale UCSF patient data of 527,273 females from 2011 to 2017, and related publications from 2000 to 2017 regarding skin conditions. The selected conditions have been confirmed with experts in the field from different research centers and hospitals. We proposed a novel framework for large-scale available public data to find the common genotypes and phenotypes of different skin conditions. The outcome of this study based on Advance Data Analytics provides information on skin conditions and their treatments to the research community and introduces new hypotheses for possible genotype and phenotype targets. The novelty of this work is a meta-analysis of different features on different skin conditions. Instead of looking at individual conditions with one or two features, which is how most of the previous works are conducted, we looked at several conditions with different features to find the common factors between them. Our hypothesis is that by finding the overlap in genotype and phenotype between different skin conditions, we can suggest using a drug that is recommended in one condition, for treatment in the other condition which has similar genes or other common phenotypes. We identified common genes between these skin conditions and were able to find common areas for targeting between conditions, such as common drugs. Our work has implications for discovery and new hypotheses to improve health quality, and is geared towards making Big Data useful. Full article
(This article belongs to the Special Issue Data-Driven Healthcare Research)
Figures

Figure 1

Back to Top