By Arjen Hommersom, Peter J.F. Lucas
Medicine and overall healthiness care are at present confronted with an important upward push of their complexity. this is often in part because of the growth made prior to now 3 a long time within the basic organic realizing of the reasons of healthiness and sickness on the molecular, (sub)cellular, and organ point. because the finish of the Seventies, whilst wisdom illustration and reasoning within the biomedical box grew to become a separate quarter of study, large development has been made within the improvement of tools and instruments which are ultimately capable of influence at the method drugs is being practiced.
Even although there are large modifications within the recommendations and techniques utilized by biomedical researchers, there's now an expanding tendency to proportion examine leads to phrases of formal wisdom illustration equipment, corresponding to ontologies, statistical versions, community versions, and mathematical versions. As there's an pressing want for health-care execs to make greater judgements, computer-based help utilizing this information is now changing into more and more vital. it may well even be the single method to combine study effects from different components of the spectrum of biomedical and scientific research.
The goal of this publication is to make clear advancements in wisdom illustration at assorted degrees of biomedical software, starting from human biology to scientific directions, and utilizing diverse suggestions, from likelihood conception and differential equations to common sense. The ebook begins with introductory chapters via 18 contributions equipped within the following topical sections: prognosis of ailment; tracking of wellbeing and fitness and disorder and conformance; evaluation of wellbeing and fitness and personalization; prediction and diagnosis of healthiness and disorder; therapy of disorder; and suggestions.
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Extra resources for Foundations of Biomedical Knowledge Representation: Methods and Applications
A theory of diagnosis for incomplete causal models. In: International Joint Conference on Artificial Intelligence (IJCAI 1989), pp. 1311– 1317. AAAI Press (1989) 6. : Model-based reasoning: troubleshooting. E. ) Exploring Artificial Intelligence: Survey Talks from the National Conference on Artificial Intelligence, pp. 297–346. Morgan Kaufmann, San Mateo (1988) 7. : Diagnosing multiple faults. Artif. Intell. 32, 97–130 (1987) 8. : Diagnosis in general practice: using probabilistic reasoning. Br.
It will become clear later in this chapter that there are many different definitions of the notion of diagnosis possible. Second, a diagnosis is always based on the interpretation of specific domain knowledge. Clearly, the representation of this knowledge is also an important issue. A third aspect of diagnostic reasoning, that is not always taken into account when describing diagnostic reasoning, is a strategy involving the dynamic collection of information to rule-in and rule-out particular diagnoses.
Finally, several programs have been developed that offer limited possibility to carry out diagnostic problem solving using multiple models; examples of such programs are GDE . These programs use DNSB diagnosis as their core approach. 5 Conclusions This chapter introduces the book section on the use of knowledge representation to solve the diagnostic problem. The chapter has briefly overviewed important formalisation theories which arise from the two reasoning frameworks dominating formal medicine; namely deductive and probabilistic.