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A situation where this information gathering may proof difficult in when a diagnostic decision making support system (DDSS) will have to gather such information from patient before carrying out the diagnostic procedure.
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Its topics included intelligent agents, fuzzy logic, multi-agent systems, artificial neural networks, genetic algorithms, expert systems, intelligent decision making support systems, information retrieval systems, geographic information systems, and knowledge management systems.
At the same time, the important role of these models in developing a computerized decision making support systems for clinicians was discussed.
One of the tasks of decision-making support systems is to develop methods that help the designer select a solution among a set of actions, e.g. by constructing a function expressing his/her preferences over a set of potential solutions.
For example, Tucker and Miller [ 25] used HIMSS data but defined EMR adoption as complete adoption of four components including computerized practitioner order entry, clinical data repositories, clinical decision making support software, and systems designed to digitize physician documentation.
Finally, a maintenance decision-making support system (MDMSS) is developed based on the proposed model.
In his doctoral research he developed a design decision-making support system for early-stage design considering natural ventilation potential and embedded the system in Rhino Grasshopper.
This paper describes iDecisionSupport, a collaborative decision-making support system that is characterized by and provides safety, usefulness, efficiency and usability.
These four modules are integrated into the multi-objective decision-making support system for maintenance strategies for deteriorating reinforced concrete buildings (MDMS-RCB).
The water quantity and water quality conjunctive dispatching decision-making support system, which ensures flood control, water supply and eco-aimed dispatching, is built by combining the water diversion with flood control dispatching and strengthening water resources monitoring and forecasting.
Regarding cervical cancer, an intelligent decision making system may support physicians to improve the selection of protocols for monitoring, diagnosing, and treating women with intraepithelial lesions or cervical cancer or even support the rational selection and the patient-specific follow-up decision making for women who have been treated for high-grade lesions.
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