The openEHR specifications are designed to support implementation of flexible and interoperable Electronic Health Record (EHR) systems. Despite the increasing number of solutions based on the openEHR specifications, it is difficult to find publicly available healthcare datasets in the openEHR format that can be used to test, compare and validate different data persistence mechanisms for openEHR. To foster research on openEHR servers, we present the openEHR Benchmark Dataset, ORBDA, a very large healthcare benchmark dataset encoded using the openEHR formalism. To construct ORBDA, we extracted and cleaned a de-identified dataset from the Brazilian National Healthcare System (SUS) containing hospitalisation and high complexity procedures information and formalised it using a set of open-EHR archetypes and templates. Then, we implemented a tool to enrich the raw relational data and convert it into the openEHR model using the openEHR Java reference model library. The ORBDA dataset is available in composition, versioned composition and EHR openEHR representations in XML and JSON formats. In total, the dataset contains more than 150 million composition records. We describe the dataset and provide means to access it. Additionally, we demonstrate the usage of ORBDA for evaluating inserting throughput and query latency performances of some NoSQL database management systems. We believe that ORBDA is a valuable asset for assessing storage models for openEHR-based information systems during the software engineering process. It may also be a suitable component in future standardised benchmarking of available openEHR storage platforms.
Choosing the right professional that has to meet indeterminate requirements is a critical aspect in humanitarian development and implementation projects. This paper proposes a hybrid evaluation methodology for some non-governmental organizations enabling them to select the most competent expert who can properly and adequately develop and implement humanitarian projects. This methodology accommodates various stakeholders’ perspectives in satisfying the unique requirements of humanitarian projects that are capable of handling a range of uncertain issues from both stakeholders and project requirements. The criteria weights are calculated using a two-step multi-criteria decision-making method: (1) fuzzy analytical hierarchy process for the evaluation of the decision maker weights coupled with (2) technique for order preference by similarity to ideal solution to rank the alternatives which provide the ability to take into account both quantitative and qualitative evaluations. Sensitivity analysis have been developed and discussed by means of a real case of expert selection problem for a non-profit organisation. The results show that the approach allows a decrease in the uncertainty associated with decision-making, which proves that the approach provides robust solutions in terms of sensitivity analysis.
We consider the Dirichlet problem for a partial differential equation involving the Jacobian determinant in two dimensions of space. The problem consists in finding a vector-valued function such that the determinant of its gradient is given pointwise in a bounded domain, together with essential boundary conditions. This problem was initially considered in Dacorogna and Moser [Ann. Inst. H. Poincaré Anal. Non Linéaire, 7 (1990), pp. 1--26], and several theoretical generalizations have been derived since. In this work, we design a numerical algorithm for the approximation of the solution of such a problem for various kinds of boundary data. The proposed method relies on an augmented Lagrangian algorithm with biharmonic regularization, and low order mixed finite element approximations. An iterative method allows us to decouple the nonlinearity and the differential operators. Numerical experiments show the capabilities of the method for benchmarks and then for more demanding test problems.
Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorisation method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries. Our system provides competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP among the participants, being +22.3% higher than the median infAP of the participant’s best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system’s performance increasing our baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively. Our similarity measure algorithm seems to be robust, in particular compared to Divergence From Randomness framework, having smaller performance variations under different training conditions. Finally, the result categorization did not have significant impact on the system’s performance. We believe that our solution could be used to enhance biomedical dataset management systems. The use of data driven expansion methods, such as those based on word embeddings, could be an alternative to the complexity of biomedical terminologies. Nevertheless, due to the limited size of the assessment set, further experiments need to be performed to draw conclusive results.
Background: While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. Results: An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results. Conclusions: Our proposed representation learning approach leverages terminological embeddings to capture semantic similarity. Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem.
Web platforms and applications are generating a normalized environment for users to consume information. This process of making the Internet experience “clickable” and “fun” comes at a price: we are less inclined to face important decisions on how applications work, data are handled, or how algorithms decide. This article will examine the possibility of shifting from predetermined results to open and descriptive applications. We inscribe this effort in the context of the Social Semantic Web (s2w) and aim to add a pragmatic approach relying on the importance of humanly created semantics, as a means to fulfil the vision of the s2w. To achieve this we have introduced a descriptive protocol (CoWaBoo) that revisits fundamental web user activities such as search, classification, group formation and valorization of participation. This article will build on the 2017 results from our university group course and, particularly, the prototype applications created through the API of CoWaBoo during the same period. Our aim is to shift our attention from how things end up on the web, to how things become, regarding software and applications development. The conclusions of this article will provide further questions for the development of the protocol (CoWaBoo), its applications and the competencies that we need to develop to become actors in the s2w.
The Dial-a-Ride Problem (DARP) consists of designing vehicle routes and schedules for customers with special needs and/or disabilities. The DARP with Electric Vehicles and battery swapping stations (DARP-EV) concerns scheduling a fleet of EVs to serve a set of pre-specified transport requests during a certain planning horizon. In addition, EVs can be recharged by swapping their batteries with charged ones from any battery-swap stations. We propose three enhanced Evolutionary Variable Neighborhood Search (EVO-VNS) algorithms to solve the DARP-EV. Extensive computational experiments highlight the relevance of the problem and confirm the efficiency of the proposed EVO-VNS algorithms in producing high quality solutions.
Consumer behaviour is often complex and even sometimes not economically rational. Wrongly, the first techno-economic energy planning models assumed the economic rationality hypothesis and, therefore, represented consumers’ behaviour incorrectly. Nevertheless, the current trend is to couple these models with behavioural approaches that were specially developed to describe the real consumer choices. A novel approach was recently proposed, where a classical energy model is coupled with a share of choice model. This new approach has however two weaknesses. First, the share of choice increases the computational complexity as it necessitates additional binary variables for the modelling. Second, for complex models, the inclusion of the share of choice can lead to non-linearity and hence to severe computational problems. In the present paper, we propose to improve this method by externalizing the share of choice. Doing so, the number of binary variable will be reduced and the linearity property will be kept even for complex models.
In the context of home healthcare services, patients may need to be visited multiple times by different healthcare specialists who may use a fleet of heterogeneous vehicles. In addition, some of these visits may need to be synchronized with each other for performing a treatment at the same time. We call this problem the Heterogeneous Fleet Vehicle Routing Problem with Synchronized visits (HF-VRPS). It consists of planning a set of routes for a set of light duty vehicles running on alternative fuels. We propose three population-based hybrid Artificial Bee Colony metaheuristic algorithms for the HF-VRPS. These algorithms are tested on newly generated instances and on a set of homogeneous VRPS instances from the literature. Besides producing quality solutions, our experimental results illustrate the trade-offs between important factors, such as CO2 emissions and driver wage. The computational results also demonstrate the advantages of adopting a heterogeneous fleet rather than a homogeneous one for the use in home healthcare services.
