Fuzzy Set Theory in Medicine

Fuzzy set theory has a number of properties that make it suitable for formalizing the uncertain information upon which medical diagnosis and treatment is usually based. Firstly, it allows us to define inexact medical entities as fuzzy sets. Secondly, it provides a linguistic approach with an excellent approximation to texts. Finally, fuzzy logic offers powerful reasoning methods capable of drawing approximate inferences. These facts suggest that fuzzy set theory might be a suitable basis for the development of a computerized diagnosis and treatment-recommendation system. This is borne out by trials performed with the medical expert system CADIAG-2, which uses fuzzy set theory to formalize medical relationships

Clinical monitoring with fuzzy automata

Abstract: In this paper, a framework for an intelligent bedside monitor is presented. The monitor derives an abstraction of the current status of a patient by performing fuzzy state transitions on pre-processed input continuously supplied by clinical instrumentation. So far, an implementation called DiaMon-1 has been used for off-line evaluation of data of patients suffering from the adult respiratory distress syndrome (ARDS)

Extending the Medical Concept of Reference Intervals using Fuzzy Predicates

Abstract Expert systems for medical applications have to deal with medical concepts such as "normal range", "elevated", or "reduced". These concepts, although backed by a profound medical background based on reference intervals, are defined manually by physicians using interval-based representation. This approach is usually not feasible in largescale applications. In the present study we describe a method to generate fuzzy-logic-based predicates founded on historic medical data, using a combination of established statistical methods and cluster analyses to generate concepts that correspond to established laboratory standards and the physician's interpretation. We also describe visualization techniques which help the physician to analyze and adapt the results according to clinical needs. Finally, a case study using actual laboratory data from 562 hepatitis patients is presented

Data correction pre-processing for electronically stored blood culture results: Implications on microbial spectrum and empiric antibiotic therapy

<p>Abstract</p> <p>Background</p> <p>The outcome of patients with bacteraemia is influenced by the initial selection of adequate antimicrobial therapy. The objective of our study was to clarify the influence of different crude data correction methods on a) microbial spectrum and ranking of pathogens, and b) cumulative antimicrobial susceptibility pattern of blood culture isolates obtained from patients from intensive care units (ICUs) using a computer based tool, MONI.</p> <p>Methods</p> <p>Analysis of 13 ICUs over a period of 7 years yielded 1427 microorganisms from positive results. Three different data correction methods were applied. Raw data method (RDM): Data without further correction, including all positive blood culture results. Duplicate-free method (DFM): Correction of raw data for consecutive patient's results yielding same microorganism with similar antibiogram within a two-week period. Contaminant-free method (CFM): Bacteraemia caused by possible contaminants was only assumed as true bloodstream infection, if an organism of the same species was isolated from > 2 sets of blood cultures within 5 days.</p> <p>Results</p> <p>Our study demonstrates that different approaches towards raw data correction – none (RDM), duplicate-free (DFM), and a contaminant-free method (CFM) – show different results in analysis of positive blood cultures. Regarding the spectrum of microorganisms, RDM and DFM yielded almost similar results in ranking of microorganisms, whereas using the CFM resulted in a clinically and epidemiologically more plausible spectrum.</p> <p>Conclusion</p> <p>For possible skin contaminants, the proportion of microorganisms in terms of number of episodes is most influenced by the CFM, followed by the DFM. However, with exception of fusidic acid for gram-positive organisms, none of the evaluated correction methods would have changed advice for empiric therapy on the selected ICUs.</p

Consistency checking of binary categorical relationships in a medical knowledge base

Abstract Moser, W. and K-P. Adlassnig, Consistency checking of binary categorical relationships in a medical knowledge base, Artificial Intelligence in Medicine 4 (1992) 389-407. Knowledge bases of medical expert systems have grown to such an extent that formal methods to verify their consistency seem highly desirable; otherwise, decision results of such expert systems are not reliable and contradictory entries in the knowledge base may cause erroneous conclusions. Tbis paper presents a new formalization of the finding/finding, finding/disease, and disease/disease relationships of the medical expert system CALXAG-1. This formalization also helps to clarify the differences between the application of propositional logic and of quantificational logic to capture the meaning of some fundamental categorical relationships in the area of medical diagnostics. Moreover, this formalization leads to very simple yet provably correct and complete algorithms to check the consistency of a medical knowledge base containing a set of these relationships

Towards an interoperable information infrastructure providing decision support for genomic medicine

Genetic dispositions play a major role in individual disease risk and treatment response. Genomic medicine, in which medical decisions are refined by genetic information of particular patients, is becoming increasingly important. Here we describe our work and future visions around the creation of a distributed infrastructure for pharmacogenetic data and medical decision support, based on industry standards such as the Web Ontology Language (OWL) and the Arden Syntax

Processing Gradual Information with Fuzzy Arden Syntax

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Uptake of Radionuclides by Bryophytes in the Chornobyl Exclusion Zone

The “Chernobyl nuclear disaster” released huge amounts of radionuclides, which are still detectable in plants and sediments today. Bryophytes (mosses) are primitive land plants lacking roots and protective cuticles and therefore readily accumulate multiple contaminants, including metals and radionuclides. This study quantifies 137Cs and 241Am in moss samples from the cooling pond of the power plant, the surrounding woodland and the city of Prypiat. Activity concentrations of up to 297 Bq/g (137Cs) and 0.43 Bq/g (241Am) were found. 137Cs contents were significantly higher at the cooling pond, where 241Am was not detectable. Distance to the damaged reactor, amount of original fallout, presence of vascular tissue in the stem or taxonomy were of little importance. Mosses seem to absorb radionuclides rather indiscriminately, if available. More than 30 years after the disaster, 137Cs was washed out from the very top layer of the soil, where it is no more accessible for rootless mosses but possibly for higher plants. On the other hand, 137Cs still remains solved and accessible in the cooling pond. However, 241Am remained adsorbed to the topsoil, thus accessible to terrestrial mosses, but precipitated in the sapropel of the cooling pond

Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies

Background: Every year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics. Methods: We developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles. Results: Our methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners. Conclusions: The ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of pharmacogenomic knowledge, as well as to identify errors, inconsistencies and insufficient definitions in source data sets or individual patient data. Our study highlights both advantages and potential practical issues with such an ontology-based approach

Biomechanical properties of fishing lines of the glowworm Arachnocampa luminosa (Diptera; Keroplatidae)

Animals use adhesive secretions in highly diverse ways, such as for settlement, egg anchorage, mating, active or passive defence, etc. One of the most interesting functions is the use of bioadhesives to capture prey, as the bonding has to be performed within milliseconds and often under unfavourable conditions. While much is understood about the adhesive and biomechanical properties of the threads of other hunters such as spiders, barely anything is documented about those of the New Zealand glowworm Arachnocampa luminosa. We analysed tensile properties of the fishing lines of the New Zealand glowworm Arachnocampa luminosa under natural and dry conditions and measured their adhesion energy to different surfaces. The capture system of A. luminosa is highly adapted to the prevailing conditions (13–15 °C, relative humidity of 98%) whereby the wet fishing lines only show a bonding ability at high relative humidity (>80%) with a mean adhesive energy from 20–45 N/m and a stronger adhesion to polar surfaces. Wet threads show a slightly higher breaking strain value than dried threads, whereas the tensile strength of wet threads was much lower. The analyses show that breaking stress and strain values in Arachnocampa luminosa were very low in comparison to related Arachnocampa species and spider silk threads but exhibit much higher adhesion energy values. While the mechanical differences between the threads of various Arachnocampa species might be consequence of the different sampling and handling of the threads prior to the tests, differences to spiders could be explained by habitat differences and differences in the material ultrastructure. Orb web spiders produce viscid silk consisting of β-pleated sheets, whereas Arachnocampa has cross-β–sheet crystallites within its silk. As a functional explanation, the low tear strength for A. luminosa comprises a safety mechanism and ensures the entire nest is not pulled down by prey which is too heavy