Long-term care service for old people is in great demand in most of the aging societies. The number of nursing homes residents is increasing while the number of care providers is limited. Due to the care worker shortage, care to vulnerable older residents cannot be fully tailored to the unique needs and preference of each individual. This may bring negative impacts on health outcomes and quality of life among institutionalized older people. To improve care quality through personalized care planning and delivery with limited care workforce, we propose a new care planning model assisted by artificial intelligence. We apply bandit algorithms which optimize the clinical decision for care planning by adapting to the sequential feedback from the past decisions. We evaluate the proposed model on empirical data acquired from the Systems for Person-centered Elder Care (SPEC) study, a ICT-enhanced care management program.
We study a novel variant of a contextual bandit problem with multi-dimensional reward feedback formulated as a mixed-effects model, where the correlations between multiple feedback are induced by sharing stochastic coefficients called random effects. We propose a novel algorithm, Mixed-Effects Contextual UCB (ME-CUCB), achieving tildeO(d sqrt(mT)) regret bound after T rounds where d is the dimension of contexts and m is the dimension of outcomes, with either known or unknown covariance structure. This is a tighter regret bound than that of the naive canonical linear bandit algorithm ignoring the correlations among rewards. We prove a lower bound of Omega(d sqrt(mT)) matching the upper bound up to logarithmic factors. To our knowledge, this is the first work providing a regret analysis for mixed-effects models and algorithms involving weighted least-squares estimators. Our theoretical analysis faces a significant technical challenge in that the error terms do not constitute martingales since the weights depend on the rewards. We overcome this challenge by using covering numbers, of theoretical interest in its own right. We provide numerical experiments demonstrating the advantage of our proposed algorithm, supporting the theoretical claims.
The purpose of this research is to analyze the publicity of the private open space of high-rise office buildings in seoul. The contents of this research consist of two main parts. The first part is to classify the personal behavior's characteristics and to investigate the user's satisfaction of the publicity in the private open space through the case study of samples in seoul. The second part is to analyze the correlations between the personal behavior's characteristics and the user's satisfaction of the publicity in the private open space of high-rise office buildings. In conclusion, this research will contribute to establish the planning methods of the private open space of high-rise office buildings and to promote the quality of urban residential environment in Korea.
Purpose: The purpose of this research is to bring qualitative improvement to future school facilities in Green School Project by analyzing architectural characteristics of 6 green schools in Chungbuk province. Method: This research conducted drawing analysis, site visit and user interviews for the 6 samples of Green School projects in Chungbuk province performed between 2009-2014. A post occupancy evaluation was given by means of questionnaire from facility managers, school faculty members, and students Result: Efforts to achieve eco-friendly school by planting, material change in paving and bike facility was well accepted but roof garden was ruled out at execution stage because of danger and maintenance issues. Eco-pond was welcomed by middle and high-schools but elementary schools are scared away. User satisfaction was high on Energy-saving facilities and was below average on maintenance of ‘Green School’. Needs of environmental education utilizing ‘Green School’, eco-friendly and energy saving facilities is sympathized, and there were some opinions to expand applicable items and to adapt a master plan for betterment of old schools.
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