Biomedical subjects
Publications and source records attributed to K Sadegh-Zadeh.
The concept of medical diagnosis is reconstructed and its syntax, semantics, and pragmatics are analyzed. It is shown that medical diagnosis is relative to a complex diagnostic structure part of which are the method of diagnostic reasoning and the knowledge base used. Clinical knowledge-based systems research is interpreted as the advent of an engineering science of clinical practice.
A metric space - dubbed the fuzzy polynucleotide space - is presented for diagnostic purposes in the widest sense to measure the degree of difference and similarity between sequences of nucleic acids. To this end, these acids are transformed to ordered fuzzy sets. They thus become representable as points in n-dimensional unit hypercubes that may be endowed with various metrics. In this way, genetic information in particular and genetics in general become amenable to fuzzy theory, geometry, and topology.
The notions of health, illness, and disease are fuzzy-theoretically analyzed. They present themselves as non-Aristotelian concepts violating basic principles of classical logic. A recursive scheme for defining the controversial notion of disease is proposed that also supports a concept of fuzzy disease. A sketch is given of the prototype resemblance theory of disease.
Recent advances in the foundations of fuzzy theory facilitated by Bart Kosko's discovery of the fuzzy hypercube are of high relevance to artificial intelligence in medicine research. The new concepts of set inclusion, equality, and similarity are therefore reported in this paper. A unifying notion of a Lotfi Zadeh space is also introduced to show that Bart Kosko's fuzzy hypercube is a Zadeh space.
Intelligent clinical judgement is one of the main goals of medical artificial intelligence. To achieve that goal requires a reliable nosological language. Such a language is not yet available, however. In this paper the notions of health, disease, and nosology are analyzed. It is shown that health and disease as generic concepts, and also individual disease entities are best understood as fuzzy sets. Clinical language and linguistics, nosology and diagnosis may thus become directly amenable to fuzzy theory.
The concept of etiology is analyzed and the possibilities and limitations of deterministic, probabilistic, and fuzzy etiology are explored. Different kinds of formal structures for the relation of causation are introduced which enable us to explicate the notion of cause on qualitative, comparative, and quantitative levels. The conceptual framework developed is an approach to a theory of causality that may be useful in etiologic research, in building nosological systems, and in differential diagnosis, therapeutic decision-making, and controlled clinical trials. The bearings of the theory are exemplified by examining the current Chlamydia pneumoniae hypothesis on the incidence of myocardial infarction.
Progress in the theory and practice of artificial intelligence in medicine requires awareness of basic issues in medical problem solving. To stimulate discussion and research on this subject, in a series of articles some logical, methodological and meta-theoretical problems of clinical practice will be studied. The present paper reconstructs clinical decision-making as a computable process of action planning.
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What follows is a brief comment on Ludwik Fleck's paper on the foundations of medical knowledge translated by Thaddeus J. Trenn in this issue. Since the original is much older than I am, I have some scruples in presenting the critical thoughts which occurred to me when I read it a few years ago. Despite the criticism, I am very sympathetic to most of what Fleck has told us in his tragically neglected work. Two facts make Fleck's tragedy even more disturbing: (i) others have given rise to post-Fleck revolutions in epistemology by exploring his ideas while omitting proper references to him, and (ii) sociology of science, precisely what Fleck wanted to promote, has emerged without his work being operative in any sense. In my commentary, I shall examine his concept of social conditioning of knowledge.
The terms "multiconditional genesis" and "multifactorial genesis" have been frequently used in medical literature in recent decades. They are, however, only vague words because they have not been defined. In the present paper an explication leading to such definition is undertaken. The explication is based on the concept of statistical relevance. First the concepts of causal relevance, causal pseudorelevance and causal irrelevance are introduced. Positive causal relevance and negative causal relevance are distinguished. Multifactorial genesis is then defined as the generation of an event Z by n greater than 1 events X, Y1, ..., Yn, where (1) the complex event Y1& .. & Yn is positively causally relevant for Z with respect to X, and (2) each Yi is positively causally relevant in Z with respect to X & Y1 &..& Yi. Finally a quantitative concept of causality is outlined.
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