Formal representation of complex SNOMED CT expressions

<p>Abstract</p> <p>Background</p> <p>Definitory expressions about clinical procedures, findings and diseases constitute a major benefit of a formally founded clinical reference terminology which is ontologically sound and suited for formal reasoning. SNOMED CT claims to support formal reasoning by description-logic based concept definitions.</p> <p>Methods</p> <p>On the basis of formal ontology criteria we analyze complex SNOMED CT concepts, such as "Concussion of Brain with(out) Loss of Consciousness", using alternatively full first order logics and the description logic <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1472-6947-8-S1-S9-i1"><m:semantics><m:mrow><m:mi>ℰ</m:mi><m:mi>ℒ</m:mi></m:mrow><m:annotation encoding="MathType-MTEF"> MathType@MTEF@5@5@+=feaagaart1ev2aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaWenfgDOvwBHrxAJfwnHbqeg0uy0HwzTfgDPnwy1aaceaGae8hmHuKae8NeHWeaaa@37B1@</m:annotation></m:semantics></m:math></inline-formula>.</p> <p>Results</p> <p>Typical complex SNOMED CT concepts, including negations or not, can be expressed in full first-order logics. Negations cannot be properly expressed in the description logic <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1472-6947-8-S1-S9-i1"><m:semantics><m:mrow><m:mi>ℰ</m:mi><m:mi>ℒ</m:mi></m:mrow><m:annotation encoding="MathType-MTEF"> MathType@MTEF@5@5@+=feaagaart1ev2aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaWenfgDOvwBHrxAJfwnHbqeg0uy0HwzTfgDPnwy1aaceaGae8hmHuKae8NeHWeaaa@37B1@</m:annotation></m:semantics></m:math></inline-formula> underlying SNOMED CT. All concepts concepts the meaning of which implies a temporal scope may be subject to diverging interpretations, which are often unclear in SNOMED CT as their contextual determinants are not made explicit.</p> <p>Conclusion</p> <p>The description of complex medical occurrents is ambiguous, as the same situations can be described as (i) a complex occurrent <it>C </it>that has <it>A </it>and <it>B </it>as temporal parts, (ii) a simple occurrent <it>A' </it>defined as a kind of A followed by some <it>B</it>, or (iii) a simple occurrent <it>B' </it>defined as a kind of <it>B </it>preceded by some <it>A</it>. As negative statements in SNOMED CT cannot be exactly represented without a (computationally costly) extension of the set of logical constructors, a solution can be the reification of negative statments (e.g., "Period with no Loss of Consciousness"), or the use of the SNOMED CT context model. However, the interpretation of SNOMED CT context model concepts as description logics axioms is not recommended, because this may entail unintended models.</p

Avian influenza virus risk assessment in falconry

<p>Abstract</p> <p>Background</p> <p>There is a continuing threat of human infections with avian influenza viruses (AIV). In this regard falconers might be a potential risk group because they have close contact to their hunting birds (raptors such as falcons and hawks) as well as their avian prey such as gulls and ducks. Both (hunting birds and prey birds) seem to be highly susceptible to some AIV strains, especially H5N1. We therefore conducted a field study to investigate AIV infections in falconers, their falconry birds as well as prey birds.</p> <p>Findings</p> <p>During 2 hunting seasons (2006/2007 and 2007/2008) falconers took tracheal and cloacal swabs from 1080 prey birds that were captured by their falconry birds (n = 54) in Germany. AIV-RNA of subtypes H6, H9, or H13 was detected in swabs of 4.1% of gulls (n = 74) and 3.8% of ducks (n = 53) using RT-PCR. The remaining 953 sampled prey birds and all falconry birds were negative. Blood samples of the falconry birds tested negative for AIV specific antibodies. Serum samples from all 43 falconers reacted positive in influenza A virus-specific ELISA, but remained negative using microneutralisation test against subtypes H5 and H7 and haemagglutination inhibition test against subtypes H6, H9 and H13.</p> <p>Conclusion</p> <p>Although we were able to detect AIV-RNA in samples from prey birds, the corresponding falconry birds and falconers did not become infected. Currently falconers do not seem to carry a high risk for getting infected with AIV through handling their falconry birds and their prey.</p

The Systematized Nomenclature of Medicine, Clinical Terms (SNOMED CT)

Abstract. This paper presents a brief initial look at some of the possible benefits and barriers to using OWL as the language for the development, dissemination and implementation of terminological knowledge in the domain of health and health care. In particular, this assessment is made from the perspective of the author’s role in the development of the Systematized Nomenclature of Medicine (SNOMED). To date, SNOMED has developed and adopted its own special-purpose syntax and formats for terminology development, exchange and distribution. Its representation language has limited expressivity yet is not expressible by any dialect of OWL 1.0. With the evolution to OWL 1.1, the barriers to using OWL for knowledge representation have been resolved. However, partly because of SNOMED’s very large size, there remain barriers t

Creating Decision Criteria From Examples: The CRiteria Learning System (Crls)

123 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1988.This thesis describes research on a machine learning approach to automated knowledge acquisition. It focuses on the needs and expectations of problem-solvers in the domain of medicine. It outlines an approach to learning criteria-based knowledge from examples and describes the implementation of a program called the CRiteria Learning System (CRLS) which learns rules in the form of criteria tables. The program learns with a bias for unate (monotone) boolean functions which display non-equivalence symmetry. These biases are described along with their applicability to the problem of learning decision criteria.The thesis details the results of the application of CRLS to ten different biomedical problem domains, and shows that the unate bias results in more comprehensible decision rules which have better diagnostic performance than rules induced with a conjunctive bias. The system produces rules that are simple, understandable, and have appropriately tuned diagnostic performance. Comparison with other machine learning programs shows that CRLS also requires less processing time. The explanation for these very favorable results is related to the appropriateness and strength of the two components of learning bias applied here: unateness and non-equivalence symmetry. It is concluded that criteria tables can be learned efficiently by machine learning techniques; they appear to be especially appropriate in biomedical domains.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD