scholarly journals A Knowledge Management and Decision Support Model for Enterprises

2011 ◽  
Vol 2011 ◽  
pp. 1-16 ◽  
Author(s):  
Patrizia Ribino ◽  
Agnese Augello ◽  
Giuseppe Lo Re ◽  
Salvatore Gaglio
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Planning Process ◽  
Semantic Space ◽  
Knowledge Bases ◽  
Data Driven ◽  
Knowledge Representation And Reasoning ◽  
Decision Support Model ◽  
Intelligent Search ◽  
Decision Support Process

We propose a novel knowledge management system (KMS) for enterprises. Our system exploits two different approaches for knowledge representation and reasoning: a document-based approach based on data-driven creation of a semantic space and an ontology-based model. Furthermore, we provide an expert system capable of supporting the enterprise decisional processes and a semantic engine which performs intelligent search on the enterprise knowledge bases. The decision support process exploits the Bayesian networks model to improve business planning process when performed under uncertainty.

scholarly journals Formal Ontologies in Biomedical Knowledge Representation

2013 ◽  
Vol 22 (01) ◽  
pp. 132-146 ◽  
Author(s):  
L. Jansen ◽  
S. Schulz
Keyword(s):  
Decision Support ◽  
Knowledge Representation ◽  
Description Logics ◽  
Clinical Decision Support Systems ◽  
Knowledge Bases ◽  
Medical Decision ◽  
Knowledge Representation And Reasoning ◽  
Digital Information ◽  
Biomedical Knowledge ◽  
Formal Ontologies

Summary Objectives: Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are often misinterpreted to encode all kinds of statements, including those which are not ontological. Method: We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. Results: We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems.

scholarly journals EXPERT AND KNOWLEDGE SYSTEMS AND DATABASES OF THE BEST PRACTICE

2004 ◽  
Vol 10 (3) ◽  
pp. 88-95 ◽  
Author(s):  
Artūras Kaklauskas ◽  
Edmundas Kazimieras Zavadskas ◽  
Leonarda Gargasaite
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Best Practice ◽  
Expert Knowledge ◽  
Knowledge Bases ◽  
Knowledge Systems ◽  
Web Based ◽  
The Web

Investigations on the similarities and differences of expert, knowledge management and decision support systems are presented in the paper. Explicit and tacit knowledge is analysed. The benefit of knowledge management systems, their development and implementation are analysed. The aspects of the best practice and its knowledge bases and databases are described. Practical possibilities to apply the knowledge systems are presented. On the base of practice acquired during the FP 6 project INTELCITIES the database of the best practice and the web‐based decision support system for real estate are developed. On the base of the BPJTA in PuBs project the database of the best practice and the web‐based decision support system for retrofit of public buildings are developed.

scholarly journals Contributions from the 2017 Literature on Clinical Decision Support

2018 ◽  
Vol 27 (01) ◽  
pp. 122-128 ◽  
Author(s):  
V. Koutkias ◽  
J. Bouaud ◽  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Review Process ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Data Driven ◽  
Editorial Team ◽  
Reference List ◽  
Surgical Team ◽  
The Impact

Objectives: To summarize recent research and select the best papers published in 2017 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook. Methods: A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation. Results: Among the 1,194 retrieved papers, the entire review process resulted in the selection of four best papers. The first paper studies the impact of recency and of longitudinal extent of electronic health record (EHR) datasets used to train a data-driven predictive model of inpatient admission orders. The second paper presents a decision support tool for surgical team selection, relying on the history of surgical team members and the specific characteristics of the patient. The third paper compares three commercial drug-drug interaction knowledge bases, particularly against a reference list of highly-significant known interactions. The fourth paper focuses on supporting the diagnosis of postoperative delirium using an adaptation of the “anchor and learn” framework, which was applied in unstructured texts contained in EHRs. Conclusions: The conducted review illustrated also this year that research in the field of CDSS is very active. Of note is the increase in publications concerning data-driven CDSSs, as revealed by the review process and also reflected by the four papers that have been selected. This trend is in line with the current attention that “Big Data” and data-driven artificial intelligence have gained in the domain of health and CDSSs in particular.

Assessing desalination as a sustainable alternative using a multiple criteria decision support model

2008 ◽  
Vol 3 (3) ◽  
Author(s):  
M. B. Fernandes ◽  
M. C. Almeida ◽  
A. G. Henriques
Keyword(s):  
Decision Support ◽  
Energy Production ◽  
Fossil Fuels ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
High Energy ◽  
Economic Benefits ◽  
Multiple Criteria ◽  
Water Production ◽  
Decision Support Model

Desalination technologies provide an alternative for potable water production, having significant potential for application where fresh water scarcity exists. Potential benefits have to be balanced with other factors, such as high costs, high energy consumption, and significant environmental impacts, for the understanding of real risks and gains of desalination within the context of integrated water resources management. Multiple factors can be considered when analysing the viability of a desalination project but often a limited approach is used. The complexity in the analysis lies in finding the alternatives that obey to multiple objectives (e.g. reduced environmental impact, social acceptance, less cost associated). In this paper, development of a methodology based on multiple criteria decision support system for the evaluation and ranking the potential of desalination technologies is described and applied to a Portuguese case study. Relevant factors to the selection of desalination technologies were identified using SWOT analysis and the MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) approach was applied. Technical alternatives considered include reverse osmosis and multi-effect desalination (MED), together with energy production by fossil fuels or solar energy. Production of water by conventional approaches was also considered. Results, for non-economic benefits, show higher score for MED solar but, in the cost-benefit analysis, conventional methods of water production have higher ranking since costs of renewable energies are not yet competitive. However, even if not preferred in economic terms, desalination is ranked significantly above the conventional approaches for non-economic criteria.

scholarly journals Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

2021 ◽  
Vol 13 (9) ◽  
pp. 4640
Author(s):  
Seung-Yeoun Choi ◽  
Sean-Hay Kim
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Data Analytics ◽  
High Performance ◽  
Problem Definition ◽  
Design Expert ◽  
Decision Support Process ◽  
High Performance Building

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

scholarly journals Data-Driven Decision Support for Optimizing Cyber Forensic Investigations

2021 ◽  
Vol 16 ◽  
pp. 2397-2412
Author(s):  
Antonia Nisioti ◽  
George Loukas ◽  
Aron Laszka ◽  
Emmanouil Panaousis
Keyword(s):  
Decision Support ◽  
Data Driven ◽  
Forensic Investigations

scholarly journals Beyond Two Cultures: Cultural Infrastructure for Data-driven Decision Support

2021 ◽  
Vol 7 (1) ◽  
pp. 77-94
Author(s):  
Nikki L. B. Freeman ◽  
John Sperger ◽  
Helal El-Zaatari ◽  
Anna R. Kahkoska ◽  
Minxin Lu ◽  
...  
Keyword(s):  
Decision Support ◽  
Data Driven ◽  
Two Cultures ◽  
Cultural Infrastructure

scholarly journals Best Paper Selection

2018 ◽  
Vol 27 (01) ◽  
pp. 127-128
Keyword(s):  
Decision Support ◽  
Postoperative Delirium ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Free Text ◽  
Health Records ◽  
Support Tool ◽  
Team Selection ◽  
Order Sets

Chen JH, Alagappan M, Goldstein MK, Asch SM, Altman RB. Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets. Int J Med Inform 2017 Jun;102:71-9 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28495350/ Ebadi A, Tighe PJ, Zhang L, Rashidi P. DisTeam: A decision support tool for surgical team selection. Artif Intell Med 2017 Feb;76:16-26 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28363285/ Fung KW, Kapusnik-Uner J, Cunningham J, Higby-Baker S, Bodenreider O. Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support. J Am Med Inform Assoc 2017 Jul 1;24(4):806-12 https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocx010 Mikalsen KØ, Soguero-Ruiz C, Jensen K, Hindberg K, Gran M, Revhaug A, Lindsetmo RO, Skrøvseth SO, Godtliebsen F, Jenssen R. Using anchors from free text in electronic health records to diagnose postoperative delirium. Comput Methods Programs Biomed 2017 Dec;152:105-14 https://linkinghub.elsevier.com/retrieve/pii/S0169-2607(17)31154-9

Sign in / Sign up

Export Citation Format

Share Document