Journal of Integrated Design and Process Science

The Unified Medical Language System (UMLS) is a large and complex controlled medical terminology managed by the National Library of Medicine. The UMLS combines about 160 different terminologies into the Metathesaurus containing over 2.4 million concepts linked by about 50 million relationships. The process of merging 160 terminologies with often very different structures is a complex and error-prone task. Auditing terminologies for quality assurance is an important phase of any terminology's life cycle. Previously, we developed the Neighborhood Auditing Tool (NAT), a concept-centric browser and auditing tool for the UMLS. In this paper we discuss the "Relationship, Audit Set Builder and Concept Neighborhood Auditing Tool" (RAC-NAT), an expansion of the NAT into a multi-faceted browsing and auditing tool.

Knowledge representation using ontology has been proposed to resolve the issues of semantic heterogeneity in data interoperability and data sharing among heterogeneous systems because ontology is a powerful expressive language to represent the required knowledge about data of a specific domain and has been applied in many fields, such as Semantic Web, e-commerce and information interoperability, etc. However, building ontology for information source manually is not only hard and error-prone, but also very personal if there is no common guideline. We had proposed a framework for data interoperability using semantic Web service to resolve the semantic heterogeneity in healthcare environment. We found that learning ontology from existing information resources is a good solution to explicitly express the semantics of an information source, but some semantics may be missing during the ontology learning process. Since relational database is widely used for storing source data, in this paper a new approach of learning OWL ontology from a relational database and information embedded in an application is proposed. In order to acquire the complete conceptual information, a group of learning rules are used to obtain OWL ontology, including classes, properties, property characteristics, cardinalities and instances. A semi-automatic application called OntoApp that implemented all proposed learning rules has been developed and tested; the results show promising in knowledge learning from information source.

In this paper, visual data analysis was applied to raw medical data using probability theory to provide valuable information for preliminary diagnosis. The evolvable hardware design approach combined with information theory was applied to model an adaptive cardiovascular system. The cardiovascular system is modelled by a digital logic circuit based on ECG and ABP signal samples as input and output respectively. In our experiments, five patients' ECG and ABP data was chosen for the visual analysis. A user friendly GUI was demoed and the correlation of patient data was analyzed in the space and time domain. The digital circuit model was extrinsically evolved using genetic programming as the evolutionary algorithm and mutual information as the fitness function. In our experiments using MATLAB, we demonstrated that the data analysis could provide valuable information for preliminary diagnosis, and the proposed method could fit the input-output relationship as recorded samples piece-wise in which each piece contains monotonic input data. The model we proposed is a self reconfigurable digital circuit model based on input and output information. It's safe to conclude that the model is adaptive to changes based on different patient's unique ECG and ABP signals since the I/O information is also changed. Furthermore, a "divide and conquer" method was employed to get a more accurate piece-wise model. Experimental results show that the method is feasible, scalable, and promising as a personalized medical simulation tool.

As in the foundation and principles for complex process science and engineering, a major problem is the lack of formal specification language to treat the dynamics of modeling complex processes with its simulations, emulations and enactments. This paper defines a formal specification language which allows integrating the complex thinking with software engineering principles for guiding the characterization of minimum requirements to design technology within living complex processes. There is a lack of research in the literature referring to the model process structural complexity. Such a model complex process can be directed toward acquiring good maintainability attributes according to the principles of complex process science and engineering. In this work a Value Based Business Process Management Network Model (VBPMN) is developed to acquire directly from the target complex process codes the knowledge hidden among and within composite and elementary complex processes.

The foundation for network process evolution research is the modeling of network structure and behavior complexity. With such a model, network systems can be directed toward acquiring good maintainability attributes according to the principles of engineering. In this paper, a Process Management Network (PMN) model is developed to acquire directly from the target process codes the knowledge hidden among and within components of network systems. With the knowledge acquired by the PMN model, network structure and behavior complexity measures in terms of partitioning, restructuring and rewriting criteria are developed; a systematic process re-modularization schema is derived, and algorithms for scheduling network changes are presented. These criteria and mechanisms are used to guide the network evolution.