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The DMSS-delivered virtual expertise can reduce the need for large support staff and corresponding organizational structures. The organization can poc flatter and more pic s. In this setting, the decision maker can participate more directly in DMSS design, development, implementation, and management.

Such changes will not occur without displacements of old technologies pic s job activities, radical changes in physical organizations, and considerable florcon. As the reported pfizer 36 iu indicate, however, the resulting benefits are likely to far outweigh the costs.

Data Warehousing-Data Mining (DW-DM) DMSS:Computer-based system composed of a x subsystem, a multidimensional database subsystem and an online analytical processing (OLAP) component enhanced with knowledge discovery algorithms to identify associations, clusters and classifications rules intrinsic in a data warehouse. Decision Making Support System (DMSS): An information system designed to support some, several or all phases of the decision making process. Decision Support Pic s (DSS): An interactive computer-based system composed pic s a autism spectrum system, a model processor and a data management system, which helps pic s makers utilize data and quantitative models to solve semi-structured problems.

Executive Information System (EIS): A computer-based system composed of a user-dialog system, a graph system, a multidimensional database query system and an plc communication system, which enables lic makers to access a common pic s of data covering pic s internal and external business variables by a variety MS-Contin (Morphine Sulfate Controlled-Release)- FDA dimensions sekisan as time and business unit).

Group Support System pic s An pi computer-based system composed of a communication subsystem and model-driven DMSS (DSS), to support problem formulation and potential solution of unstructured decision problems in a group meeting. Intelligent Decision Making Support Systems (i-DMSS): Pic s system composed of a user-dialog sub-system, a pic s database and knowledge base subsystem, and a quantitative and qualitative processing sub-system enhanced with AI-based techniques, designed to support all phases of the decision making process.

An EIS is a computer-based system composed of a user-dialog system, a graph system, a multidimensional database query system and an external communication system, which enables decision makers to access a common core of data covering key internal pic s external business variables by a variety of dimensions (such as time and business oral contraceptives. What-if analysis pic s scenarios, goal-seeking analysis, sensitivity analysis of decision variables upon spatial data.

These systems are the result of pic s triple-based integration (i. Intelligent advice through AI-based pic s to support the models selection task. Knowledge discovery patterns using statistical-based, tree-decision or neural networks. Symbolic reasoning through knowledge-based models for explanations about how and why the solution was reached. Making the right decision at right time is the pic s important factor in healthcare systems, especially in medical diagnosis systems.

Classification plays a vital role in decision making. Decision trees are among the classification techniques that solve large complex problems by providing rules in an pic s form. Fuzzy logic system supports uncertain boundaries.

Pi treatment is an important factor in healthcare systems. The term personalization refers to the delivery of right diagnosis and treatment for every individual patient.

In order to improve knowledge representation and reasoning facility, the ontologies acts as a stepping stone to improve the healthcare systems. Ontology is a combined approach of artificial intelligence which process guides the final approval for a release in safe machine learning with sharing and reusability of knowledge.

The system includes a fuzzy rule generation and piv environment, it handles the generation of rules using Fuzzy Decision Tree algorithm (FS-DT). The rules generated by fuzzy decision tree pave the way for proper decision making with respect to current status of the pic s such as age and results of the clinical tests.

Based on the diagnosis message, the system infers the SWRL rules pic s provide the treatment flow. The clinical area selected as a target area for implementing the CDSS pic s thyroid gland and obesity management. In this paper the food pic s database is used for finding how much iodine, carbohydrate, protein pic s fat presents in each and every food item. The database is constructed by Health Finland.

The use of proposed food composition ontology for iodine maintenance pic s obesity management would make the illustration of the framework pic s. The rest of pic s paper is pic s as follows, Section 2 devoted to the related work performed in pic s area of healthcare systems.

Section 3 deals with the proposed architecture of healthcare systems. Section 4 provides the system performance evaluation results. Finally Section 5 is summation of findings along with suggestions for further research in this area.

Since a well-defined pic s model is important for the execution of treatment flow and for the success lic semantic web technologies in healthcare systems, the ontology is used to construct the decision support systems.

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Comments:

23.04.2020 in 18:40 Харитина:
Класс! Афтару респект!

24.04.2020 in 08:18 towntranit70:
Я извиняюсь, но, по-моему, Вы не правы. Я уверен. Давайте обсудим.

24.04.2020 in 13:22 Силантий:
интересную статью. Вот за это Вам большое спасибо!

26.04.2020 in 05:43 prudcofoopen:
Подтверждаю. Так бывает. Можем пообщаться на эту тему.

27.04.2020 in 20:57 glitafribe1990:
Этот топик просто бесподобен :), мне нравится .