By Marius Fieschi (auth.)
Expert platforms represent a study zone that is presently increasing. This booklet is predicated mostly on paintings undertaken for my doctoral thesis and makes an attempt to set out in simply understood language different tools of information illustration utilized in assorted structures. How ever, because the box for purposes is gigantic and touches on many disciplines (engineering technological know-how, computing, geology, medication etc.) purely these platforms with clinical functions are offered. the second one a part of this ebook is dedicated to distinctive dialogue of 1 professional method built during this division: SPHINX. I desire to thank all those that have given me their help, their criticisms and proposals: Dominique Fieschi, Michel Joubert, Genevieve Botti, Michel Roux, Jean-Louis Lauriere, in addition to the CNRS which supported the ATP professional platforms team with anyone supply. Marius Fieschi Foreword This paintings offers with 'Expert platforms' within the realm of medication. The word 'Expert procedure' describes a data method not just by way of its content material but additionally when it comes to its program. as with every prevalent phrases, it truly is condensed to the purpose the place the that means can't be guessed from easily studying it. It issues platforms processing wisdom and behavior in methods with regards to these of a human professional. within the box of medication this specialist stands out as the advisor or expert to whom a relations practitioner refers a 'case' which he's not able to diagnose. The examine of specialist platforms is a department of desktop technology known as synthetic intelligence.
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Extra resources for Artificial Intelligence in Medicine: Expert Systems
For example, rules are disposed which represent inclusion relations such as 'Hashimoto's thyroiditis is an auto-immune disease'. In this case, and all other auto-immune disease, it may be very disadvantageous to use them in forward chaining, that is to say, the reasoning ought if possible to reduce the number of alternatives. If it is known that a goitre accompanied by symptoms of hyperthyroidism is due to Hashimoto's thyroiditis we know from the outcome that it is an auto-immune disease. To follow the hierarchy in this direction does not lead to the combinational explosion.
The medical knowledge is represented as a network of physiological and physio-pathological states. The use of this type of knowledge gives better understanding of the mechanisms and development of the disease. However, the system does not have the capacity to use diagnostic hypotheses to direct its enquiry. It separates the process of data acquisition from that of the diagnosis. Patient symptoms are acquired by confirming (or rejecting) the states in the causal network. During this phase the system does not try to identify the diseases causing these pathological states.
It is important to stress that this form of modelling does not consider diagnosis as a simple allocation to a pre-established class of patients. The patient's state is dynamically reassessed with each visit and signs of change observed. Judging by its performance, 95% of specialists in ophthalmology favoured this system as much for its applicability as a research tool in glaucoma as for its diagnostic competence. The CASNET system has been generalized and extended in a version called EXPERT (Kulikowski and Weiss, 1982).
Artificial Intelligence in Medicine: Expert Systems by Marius Fieschi (auth.)