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Peak Flow Meter Chart Child Flowchart Examples Asthma Exacerbations Assessment And Management Of

Peak Flow Meter Chart Child Flowchart Examples Asthma Exacerbations Assessment And Management Of Readings

By Gabrielle Button at October 30 2018 00:58:25

Current research in information processing deals with programs that enable a computer to understand written or spoken information and to produce summaries, answer specific questions, or redistribute information to users interested in specific areas of this information. Essential to such programs is the ability of the system to generate grammatically correct sentences and to establish linkages between words, ideas, and associations with other ideas. Research has shown that whereas the logic of language structure-its syntax-submits to programming, the problem of meaning, or semantics, lies far deeper, in the direction of true AI.

In all cases, however, the task that the algorithm is to accomplish must be definable. That is, the definition may involve mathematical or logic terms or a compilation of data or written instructions, but the task itself must be one that can be stated in some way. In terms of ordinary computer usage, this means that algorithms must be programmable, even if the tasks themselves turn out to have no solution. In computational devices with a built-in microcomputer logic, this logic is a form of algorithm. As computers increase in complexity, more and more software-program algorithms are taking the form of what is called hard software.

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In medicine, programs have been developed that analyze the disease symptoms, medical history, and laboratory test results of a patient, and then suggest a diagnosis to the physician. The diagnostic program is an example of so-called expert systems-programs designed to perform tasks in specialized areas as a human would. Expert systems take computers a step beyond straightforward programming, being based on a technique called rule-based inference, in which preestablished rule systems are used to process the data. Despite their sophistication, systems still do not approach the complexity of true intelligent thought.

It is extremely important to continuously improve knowledge processes, by creating an environment through which they can evolve. This can only be achieved through coordination of diverse disciplines such as knowledge management, change management, expectations management, etc... It is crucial to establish an adequate process context (the combination of technologies, procedures, people, etc... that support the processes). The process context must incorporate feedback mechanisms, change evaluation procedures, process improvement methods and techniques and must be flexible, in order to be able to incorporate enhancements in an agile but controlled way.

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