By Arjen Hommersom, Peter J.F. Lucas
Medicine and well-being care are presently confronted with an important upward push of their complexity. this can be in part as a result growth made prior to now 3 a long time within the basic organic realizing of the explanations of wellbeing and fitness and affliction on the molecular, (sub)cellular, and organ point. because the finish of the Seventies, while wisdom illustration and reasoning within the biomedical box turned a separate quarter of study, large development has been made within the improvement of tools and instruments which are ultimately in a position to impression at the approach medication is being practiced.
Even even though there are large variations within the strategies and strategies utilized by biomedical researchers, there's now an expanding tendency to proportion learn leads to phrases of formal wisdom illustration equipment, comparable 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 data is now changing into more and more vital. it can even be the single technique to combine learn effects from different components of the spectrum of biomedical and medical research.
The goal of this e-book is to make clear advancements in wisdom illustration at diversified degrees of biomedical software, starting from human biology to medical directions, and utilizing varied innovations, from chance thought and differential equations to common sense. The e-book begins with introductory chapters through 18 contributions prepared within the following topical sections: prognosis of ailment; tracking of wellbeing and fitness and sickness and conformance; evaluation of overall healthiness and personalization; prediction and analysis of well-being and ailment; therapy of illness; and suggestions.
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Extra info for Foundations of Biomedical Knowledge Representation: Methods and Applications
Sample text
3 Abnormality-classification (AC) diagnosis. are classified in terms of present and absent defects. The main goal of AC diagnosis, however, remains the classification of observed findings in terms of abnormality. AC diagnosis is often referred to in the literature as heuristic classification [4], although this term is broader, since it also includes a reasoning strategy. AC diagnosis can be characterised in terms of logical deduction in a straightforward way. We shall refer to this formalisation of AC diagnosis as hypothetico-deductive diagnosis.
1. Obviously, the various models of diagnosis discussed above can also be combined. To solve real-life diagnostic problems in a domain, it is likely that a mixture of conceptual models of diagnosis as distinguished above will be required. g. both knowledge of structure and behaviour, and empirical associations, the result is known as diagnosis with multiple models. Although in the literature it is emphasised that the conceptual models of diagnosis discussed embody different forms of diagnosis, they have much in common.
2 An Introduction to Knowledge Representation and Reasoning in Healthcare 19 Consider the following toy medical knowledge base KB: ∀x((Symptom(x, coughing)∧ ∼ Symptom(x, chest-pain) ∧ Sign(x, fever)) → Disorder(x, flu)) ∀x((temp(x) > 38) → Sign(x, fever)) Then it holds that: KB∪{temp(John) = 39, Symptom(John, coughing)} NA Disorder(John, coughing) using negation by absence (NA). Note that Sign(John, fever) is true, and may be viewed as a classification of the finding temp(John) = 39; ∼ Symptom(John, chest-pain) holds due to negation by absence.