69,068 research outputs found
Ambulance Emergency Response Optimization in Developing Countries
The lack of emergency medical transportation is viewed as the main barrier to
the access of emergency medical care in low and middle-income countries
(LMICs). In this paper, we present a robust optimization approach to optimize
both the location and routing of emergency response vehicles, accounting for
uncertainty in travel times and spatial demand characteristic of LMICs. We
traveled to Dhaka, Bangladesh, the sixth largest and third most densely
populated city in the world, to conduct field research resulting in the
collection of two unique datasets that inform our approach. This data is
leveraged to develop machine learning methodologies to estimate demand for
emergency medical services in a LMIC setting and to predict the travel time
between any two locations in the road network for different times of day and
days of the week. We combine our robust optimization and machine learning
frameworks with real data to provide an in-depth investigation into three
policy-related questions. First, we demonstrate that outpost locations
optimized for weekday rush hour lead to good performance for all times of day
and days of the week. Second, we find that significant improvements in
emergency response times can be achieved by re-locating a small number of
outposts and that the performance of the current system could be replicated
using only 30% of the resources. Lastly, we show that a fleet of small
motorcycle-based ambulances has the potential to significantly outperform
traditional ambulance vans. In particular, they are able to capture three times
more demand while reducing the median response time by 42% due to increased
routing flexibility offered by nimble vehicles on a larger road network. Our
results provide practical insights for emergency response optimization that can
be leveraged by hospital-based and private ambulance providers in Dhaka and
other urban centers in LMICs
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Introducing creativity techniques and software apps to the care of people with dementia
This poster reports research to introduce creative problem solving techniques and software to the care for people with dementia in residential homes
Experimental evaluation of algorithms forsolving problems with combinatorial explosion
Solving problems with combinatorial explosionplays an important role in decision-making, sincefeasible or optimal decisions often depend on anon-trivial combination of various factors. Gener-ally, an effective strategy for solving such problemsis merging different viewpoints adopted in differ-ent communities that try to solve similar prob-lems; such that algorithms developed in one re-search area are applicable to other problems, orcan be hybridised with techniques in other ar-eas. This is one of the aims of the RCRA (Ra-gionamento Automatico e Rappresentazione dellaConoscenza) group,1the interest group of the Ital-ian Association for Artificial Intelligence (AI*IA)on knowledge representation and automated rea-soning, which organises its annual meetings since1994
Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare
For the last years, time-series mining has become a challenging issue for
researchers. An important application lies in most monitoring purposes, which
require analyzing large sets of time-series for learning usual patterns. Any
deviation from this learned profile is then considered as an unexpected
situation. Moreover, complex applications may involve the temporal study of
several heterogeneous parameters. In that paper, we propose a method for mining
heterogeneous multivariate time-series for learning meaningful patterns. The
proposed approach allows for mixed time-series -- containing both pattern and
non-pattern data -- such as for imprecise matches, outliers, stretching and
global translating of patterns instances in time. We present the early results
of our approach in the context of monitoring the health status of a person at
home. The purpose is to build a behavioral profile of a person by analyzing the
time variations of several quantitative or qualitative parameters recorded
through a provision of sensors installed in the home
Far out or alone in the crowd: Classification of selfevaluators in DEA
The units found strongly efficient in DEA studies on efficiency can be divided into self-evaluators and active peers, depending on whether the peers are referencing any inefficient units or not. The contribution of the paper starts with subdividing the selfevaluators into interior and exterior ones. The exterior self-evaluators are efficient “by default”; there is no firm evidence from observations for the classification. These units should therefore not been regarded as efficient, and be removed from the observations on efficiency scores when performing a two-stage analysis of explaining the distribution of the scores. A method for classifying self-evaluators based on the additive DEA model is developed. The application to municipal nursing- and home care services of Norway shows significant effects of removing exterior self-evaluators from the data when doing a two-stage analysis.Self-evaluator; interior and exterior self-evaluator; DEA; efficiency; referencing zone; nursing homes
Stronger instruments via integer programming in an observational study of late preterm birth outcomes
In an optimal nonbipartite match, a single population is divided into matched
pairs to minimize a total distance within matched pairs. Nonbipartite matching
has been used to strengthen instrumental variables in observational studies of
treatment effects, essentially by forming pairs that are similar in terms of
covariates but very different in the strength of encouragement to accept the
treatment. Optimal nonbipartite matching is typically done using network
optimization techniques that can be quick, running in polynomial time, but
these techniques limit the tools available for matching. Instead, we use
integer programming techniques, thereby obtaining a wealth of new tools not
previously available for nonbipartite matching, including fine and near-fine
balance for several nominal variables, forced near balance on means and optimal
subsetting. We illustrate the methods in our on-going study of outcomes of
late-preterm births in California, that is, births of 34 to 36 weeks of
gestation. Would lengthening the time in the hospital for such births reduce
the frequency of rapid readmissions? A straightforward comparison of babies who
stay for a shorter or longer time would be severely biased, because the
principal reason for a long stay is some serious health problem. We need an
instrument, something inconsequential and haphazard that encourages a shorter
or a longer stay in the hospital. It turns out that babies born at certain
times of day tend to stay overnight once with a shorter length of stay, whereas
babies born at other times of day tend to stay overnight twice with a longer
length of stay, and there is nothing particularly special about a baby who is
born at 11:00 pm.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS582 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Introduction to the 28th International Conference on Logic Programming Special Issue
We are proud to introduce this special issue of the Journal of Theory and
Practice of Logic Programming (TPLP), dedicated to the full papers accepted for
the 28th International Conference on Logic Programming (ICLP). The ICLP
meetings started in Marseille in 1982 and since then constitute the main venue
for presenting and discussing work in the area of logic programming
A derivative-free approach for a simulation-based optimization problem in healthcare
Hospitals have been challenged in recent years to deliver high quality care with limited resources. Given the pressure to contain costs,developing procedures for optimal resource allocation becomes more and more critical in this context. Indeed, under/overutilization of emergency room and ward resources can either compromise a hospital's ability to provide the best possible care, or result in precious funding going toward underutilized resources. Simulation--based optimization tools then help facilitating the planning and management of hospital services, by maximizing/minimizing some specific indices (e.g. net profit) subject to given clinical and economical constraints.
In this work, we develop a simulation--based optimization approach for the resource planning of a specific hospital ward. At each step, we first consider a suitably chosen resource setting and evaluate both efficiency and satisfaction of the restrictions by means of a discrete--event simulation model. Then, taking into account the information obtained by the simulation process, we use a derivative--free optimization algorithm to modify the given setting. We report results for a real--world problem coming from the obstetrics ward of an Italian hospital showing both the effectiveness and the efficiency of the proposed approach
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