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

By Yun Peng

Making a analysis while whatever is going mistaken with a average or m- made procedure may be tricky. in lots of fields, equivalent to medication or electr- ics, a protracted education interval and apprenticeship are required to turn into a talented diagnostician. in this time a beginner diagnostician is requested to assimilate a large number of wisdom in regards to the classification of platforms to be clinically determined. by contrast, the beginner is simply not taught the best way to cause with this data in arriving at a end or a prognosis, other than might be implicitly via ease examples. this might appear to point out that the various crucial facets of diagnostic reasoning are a kind of intuiti- dependent, good judgment reasoning. extra accurately, diagnostic reasoning could be labeled as one of those inf- ence referred to as abductive reasoning or abduction. Abduction is outlined to be a means of producing a believable cause of a given set of obs- vations or evidence. even supposing pointed out in Aristotle's paintings, the learn of f- mal elements of abduction didn't fairly commence until eventually a few century ago.

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Extra resources for Abductive Inference Models for Diagnostic Problem-Solving

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In the descriptions of the Chemical Spill Association-Based Abductive Models 27 knowledge base, the only setting factor is the months of use of a chemical, while all of the other assertions specify manifestations. Based on the above conventions, the reader should be able to appreciate that the descriptions above are a fairly accurate representation of the information given in natural-language form. Note that unlike rules in rule-based systems, the disorders and manifestations in this knowledge base are associated by a direct causal relation, having direction from causes (disorders) to effects (manifestations).

N -- 0) is used to convert symbolic probabilities to numbers in these calculations. A final score is then calculated for each active disorder based on both its setting score and its match score. Since this final numerical weighting is intended to provide only a "ballpark" indication of how likely the disorder is, it is subsequently converted back into a symbolic probability to emphasize its imprecise and heuristic nature. 4) where competing disorders are listed in the order of their final scores, indicating the likelihood of their occurrences.

From these candidate attributes, the one which appears in the largest number of descriptions of active disorders is then selected to form the basis of the next question. Some Issues 41 This simple, heuristic approach to question generation makes no claim to optimality. However, it does have certain properties that make it a useful strategy to follow. Since it selects one of the most commonly referenced attributes of active disorders, it usually produces questions that help to discriminate among the competing explanations in the current hypotheses.

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