Download Abductive Inference Models for Diagnostic Problem-Solving by Yun Peng PDF

By Yun Peng

ISBN-10: 1441986820

ISBN-13: 9781441986825

ISBN-10: 1461264502

ISBN-13: 9781461264507

Making a prognosis while whatever is going unsuitable with a ordinary or m- made procedure should be tough. in lots of fields, resembling drugs or electr- ics, an extended education interval and apprenticeship are required to develop into a talented diagnostician. in this time a beginner diagnostician is requested to assimilate a large number of wisdom in regards to the category of structures to be clinically determined. against this, the beginner just isn't taught find out how to cause with this information in arriving at a end or a prognosis, other than possibly implicitly via ease examples. this could appear to point out that a number of the crucial features of diagnostic reasoning are one of those intuiti- dependent, logic reasoning. extra accurately, diagnostic reasoning may be categorised as a kind of inf- ence referred to as abductive reasoning or abduction. Abduction is outlined to be a means of producing a believable reason for a given set of obs- vations or proof. even supposing pointed out in Aristotle's paintings, the learn of f- mal facets of abduction didn't quite commence until eventually a couple of century ago.

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Extra resources for Abductive Inference Models for Diagnostic Problem-Solving

Example text

Thus, to justify the disorder Ototoxicity Secondary to Quinine < h > , in the second competing group, the system gives the following message: 46 Computational Models for Diagnostic Problem Solving For Ototoxicity Secondary to Quinine, factors which favor this disorder include the following: Large Amounts of Quinine. This disorder is more likely to cause the following present manifestations than some of its competitors: Impaired Hearing. For disorders of intermediate likelihood < m > , such as Otosclerosis in the second group, factors that favor as well as are against (expected but absent manifestations) are listed as the justification of their ranking.

They are called manifestations in parsimonious covering theory and denoted by the set M. , mj. Some other entities, such as "battery is dead" and "fuel line is blocked", can be considered as causes of manifestations. They are called disordem, denoted by the set D, and their presence must be inferred. , di. Manifestations and disorders are connected through causal associations to form a causal network, representing domain-specific knowledge. 2 below. For a more complete car troubleshooting knowledge base, there would be many more manifestations and disorders, and they would be interrelated in a much more complicated way.

1). Disorders, indicated by nodes in set D, are causally related to intermediate pathological states (set S), and ultimately to measurable manifestations (set M).

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Abductive Inference Models for Diagnostic Problem-Solving by Yun Peng

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