137,223 research outputs found
Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges
Dementia is a chronic and degenerative condition affecting millions globally. The care of patients with dementia presents an ever continuing challenge to healthcare systems in the 21st century. Medical and health sciences have generated unprecedented volumes of data related to health and wellbeing for patients with dementia due to advances in information technology, such as genetics, neuroimaging, cognitive assessment, free texts, routine electronic health records etc. Making the best use of these diverse and strategic resources will lead to high quality care of patients with dementia. As such, machine learning becomes a crucial factor in achieving this objective. The aim of this paper is to provide a state-of-the-art review of machine learning methods applied to health informatics for dementia care. We collate and review the existing scientific methodologies and identify the relevant issues and challenges when faced with big health data. Machine learning has demonstrated promising applications to neuroimaging data analysis for dementia care, while relatively less efforts have been made to make use of integrated heterogeneous data via advanced machine learning approaches. We further indicate the future potentials and research directions of applying advanced machine learning, such as deep learning, to dementia informatics
Electronic Study Books and learning style
Attention has been drawn to the concepts of Electronic Books and Electronic Study Books. Several publications have discussed some main ideas (paradigms) for both concepts. For the Electronic Study Book as a learning environment, it is essential to consider individual modes of learning, usually termed 'learning styles'. It is argued that Electronic Study Books should be adaptable in accordance with personal learning styles. Some options will be presented to link 'styles' and 'books'. One such option is a Style Initiating Module which we are currently investigating
The Disparate Effects of Strategic Manipulation
When consequential decisions are informed by algorithmic input, individuals
may feel compelled to alter their behavior in order to gain a system's
approval. Models of agent responsiveness, termed "strategic manipulation,"
analyze the interaction between a learner and agents in a world where all
agents are equally able to manipulate their features in an attempt to "trick" a
published classifier. In cases of real world classification, however, an
agent's ability to adapt to an algorithm is not simply a function of her
personal interest in receiving a positive classification, but is bound up in a
complex web of social factors that affect her ability to pursue certain action
responses. In this paper, we adapt models of strategic manipulation to capture
dynamics that may arise in a setting of social inequality wherein candidate
groups face different costs to manipulation. We find that whenever one group's
costs are higher than the other's, the learner's equilibrium strategy exhibits
an inequality-reinforcing phenomenon wherein the learner erroneously admits
some members of the advantaged group, while erroneously excluding some members
of the disadvantaged group. We also consider the effects of interventions in
which a learner subsidizes members of the disadvantaged group, lowering their
costs in order to improve her own classification performance. Here we encounter
a paradoxical result: there exist cases in which providing a subsidy improves
only the learner's utility while actually making both candidate groups
worse-off--even the group receiving the subsidy. Our results reveal the
potentially adverse social ramifications of deploying tools that attempt to
evaluate an individual's "quality" when agents' capacities to adaptively
respond differ.Comment: 29 pages, 4 figure
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The four powers of design: A value model in design management
This analysis proposes a framework to bridge the gap between the world of designers and the world of managers. Illuminating her thesis with examples from Steelcase, Decathlon, and other companies, Brigitte Borja de Mozota parallels design's ability to differentiate, integrate, transform, and contribute to the enterprise and bottom-line results with a corporate focus on markets, processes, talent, and finances
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