A comparison of model transformation tools: Application for Transforming GRAI Extended Actigrams into UML Activity Diagrams

Integration of information and manufacturing systems is one of the great achievements of Enterprise Modelling. However, new factors, such as the fast evolution of Information and Communication Technologies (ICT) or the need to set up alliances among different types of enterprises quickly in order to benefit from market opportunities, are causing new types of problems, like interoperability, to appear in the Enterprise Modelling context. This paper shows how a model-driven approach can be useful to solve interoperability problems using model transformations. In particular, the transformation of GRAI Extended Actigrams into UML Activity Diagrams is explored using three different model transformation tools. © 2008 Elsevier B.V. All rights reserved

ISO 15531 MANDATE: A Product-process-resource based Approach for Managing Modularity in Production Management

Abstract: Managing modularity and commonality in product development more and more needs modularity and commonality in the production process, with the objectives of reducing manufacturing costs, time to market and improving quality. A critical issue is the way of managing data, information and knowledge: data most of the time structured according to data models, often using proprietary formats, leading to consistency problems for the exchanges. The use of international standards is a good way of improving quality of the information systems used in production management, since they facilitate interoperability of the software tools used. They also contribute to the integration of the production process in a product life cycle management-based approach. This study presents the ISO 15531 MANDATE standard for the exchanges o

Evaluation of a model for glycemic prediction in critically ill surgical patients.

We evaluated a neural network model for prediction of glucose in critically ill trauma and post-operative cardiothoracic surgical patients. A prospective, feasibility trial evaluating a continuous glucose-monitoring device was performed. After institutional review board approval, clinical data from all consenting surgical intensive care unit patients were converted to an electronic format using novel software. This data was utilized to develop and train a neural network model for real-time prediction of serum glucose concentration implementing a prediction horizon of 75 minutes. Glycemic data from 19 patients were used to "train" the neural network model. Subsequent real-time simulated testing was performed in 5 patients to whom the neural network model was naive. Performance of the model was evaluated by calculating the mean absolute difference percent (MAD%), Clarke Error Grid Analysis, and calculation of the percent of hypoglycemic (≤70 mg/dL), normoglycemic (>70 and <150 mg/dL), and hyperglycemic (≥150 mg/dL) values accurately predicted by the model; 9,405 data points were analyzed. The models successfully predicted trends in glucose in the 5 test patients. Clark Error Grid Analysis indicated that 100.0% of predictions were clinically acceptable with 87.3% and 12.7% of predicted values falling within regions A and B of the error grid respectively. Overall model error (MAD%) was 9.0% with respect to actual continuous glucose modeling data. Our model successfully predicted 96.7% and 53.6% of the normo- and hyperglycemic values respectively. No hypoglycemic events occurred in these patients. Use of neural network models for real-time prediction of glucose in the surgical intensive care unit setting offers healthcare providers potentially useful information which could facilitate optimization of glycemic control, patient safety, and improved care. Similar models can be implemented across a wider scale of biomedical variables to offer real-time optimization, training, and adaptation that increase predictive accuracy and performance of therapies