Towards a novel approach for High-Stake Decision Support System based on Community Contributed Knowledge Base

Author(s):  
Yannick Naudet ◽  
Thibaud Latour ◽  
Geraldine Vidou ◽  
Younes Djaghloul
Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1773
Author(s):  
Bogdan Walek ◽  
Ondrej Pektor ◽  
Radim Farana

This paper describes a novel approach in the area of evaluating suitable job applicants for various job positions, and specifies typical areas of requirement and their usage. Requirements for this decision-support system are defined in order to be used in middle-size companies. Suitable tools chosen were fuzzy expert systems, primarily the inference system Takagi-Sugeno type, which were then supplied with implementation of methods of variant multi-criteria analysis. The resulting system is a variable tool with the possibility to simply set the importance of individual selection criteria so that it can be used in various situations, primarily in repeated selection procedures for similar job positions. A strong emphasis is devoted to the explanatory module, which enables the results of the expert system to be used easily. Verification of the system on real data in cooperation with a collaborating company has proved that the system is easily usable.


2021 ◽  
Vol 11 (1) ◽  
pp. 126-136
Author(s):  
V.V. Antonov ◽  
◽  
K.A. Konev

The article discusses a decision support method using a knowledge base. The relevance of the study of issues related to decision-making in typical situations is shown. In order to increase the effectiveness of management activities, the is-sues of system integration of the regulatory framework, ontological model and knowledge base of the intelligent sub-system of decision support within the framework of the business process are considered. In support of the proposed method, a model has been formed for replenishing the knowledge base both at the structural and analytical levels, which demonstrates the connection between the most important elements of the system: the ontological model, the knowledge base and the normative subsystem. An example of using the proposed scheme is shown. To demonstrate the model of functioning of the decision support system, an algorithm for replenishing the knowledge base is proposed and described. As a conceptual basis for the formal description of the model, operations for working with knowledge are described in the set-theoretic aspect. The principles of adaptation of the ontological model as an information object for linking with the knowledge base are considered. The conceptual diagram of the general structure of the ontological model for making decisions within the framework of the business process as a set of interrelated concepts is proposed and demonstrated. An information model of a specialized database has been developed and presented, serving as a technical basis for building a knowledge base of a decision support system in a typical situation, its main structural el-ements, the principles of their interrelation and an approach to ensuring the consistency of its internal structure are de-scribed.


2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yicheng Jiang ◽  
Bensheng Qiu ◽  
Chunsheng Xu ◽  
Chuanfu Li

In many clinical decision support systems, a two-layer knowledge base model (disease-symptom) of rule reasoning is used. This model often does not express knowledge very well since it simply infers disease from the presence of certain symptoms. In this study, we propose a three-layer knowledge base model (disease-symptom-property) to utilize more useful information in inference. The system iteratively calculates the probability of patients who may suffer from diseases based on a multisymptom naive Bayes algorithm, in which the specificity of these disease symptoms is weighted by the estimation of the degree of contribution to diagnose the disease. It significantly reduces the dependencies between attributes to apply the naive Bayes algorithm more properly. Then, the online learning process for parameter optimization of the inference engine was completed. At last, our decision support system utilizing the three-layer model was formally evaluated by two experienced doctors. By comparisons between prediction results and clinical results, our system can provide effective clinical recommendations to doctors. Moreover, we found that the three-layer model can improve the accuracy of predictions compared with the two-layer model. In light of some of the limitations of this study, we also identify and discuss several areas that need continued improvement.


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