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Modules in Health Classifications
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
2017 (Engelska)Ingår i: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2017Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

In applications, for example in health care, many-valuedness modelled using quantales plays an important role. The paper presents variations of the three chain modules C-3 over unitalization of the three chain quantale (C-3 is the smallest possible quantale to model many-valuedness), thus, variations of right actions are given. From application point of view, it is then possible to choose suitable modules when modelling, for example, the causalities between disease, intervention and functioning. The effect of drug interaction in presence of multiple diseases, and as affecting functioning, adds to this complexity. Health care communities and professionals comply with a range classifications and terminologies, also including scales to qualify strength or hierarchies of evidence, in the sense of Evidence-Based Medicine (EBM), or interaction, or as related to levels of functioning. Such hierarchies adopted in health care are ad hoc as compared to the potentially algebraic and logical structures of terminologies used as part of clinical decision-making. The notion of 'evidence' in EBM would benefit from a logical enrichment. In this paper we show how these hierarchies canonically derive as actions where transitions appear as levels in hierarchies of evidence. In this sense, the Logic-Based Medicine (LBM) view is complementary to EBM, the latter indeed very well known and established, whereas LBM is yet to be recognized more broadly. We will also see how three-valuedness related to health conditions, rather than two-valuedness, is the generator of many-valuedness related to strength of evidence.

Ort, förlag, år, upplaga, sidor
IEEE, 2017.
Serie
IEEE International Conference on Fuzzy Systems, ISSN 1098-7584
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:umu:diva-146179DOI: 10.1109/FUZZ-IEEE.2017.8015430ISI: 000426449100053ISBN: 978-1-5090-6034-4 (digital)OAI: oai:DiVA.org:umu-146179DiVA, id: diva2:1206270
Konferens
IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), JUL 09-12, 2017, Naples, ITALY
Tillgänglig från: 2018-05-16 Skapad: 2018-05-16 Senast uppdaterad: 2018-06-09Bibliografiskt granskad

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Totalt: 154 träffar
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