The fuzzy cube and causal efficacy: representation of concomitant mechanisms in stroke

The DSCP-CA: a decision support computer program--cancer pain management

The purpose of this study was to develop a decision support system using fuzzy logic that would support nurses' decisions about cancer pain management, especially for ethnic minority cancer patients. The study had two phases: (1) data collection and (2) development of the decision support computer program. In the data collection phase, an Internet survey of 428 cancer patients and four ethnic-specific online forums (about 30 participants per forum) were conducted to gather data on the cancer pain experience of the four major ethnic groups in the United States. The development phase included two components: (1) development of three modules, including a knowledge base module, a decision module, and a self-adaptation module; and (2) a 3-month evaluation of the decision support computer program by oncology nurses and subsequent incorporation of their feedback into the program. The Internet survey and online forum data were processed into fuzzy and crisp data sets, and ethnic-specific algorithms for the decision module were developed. Using the self-adaptation module, the decision support computer program was further refined as additional data were processed. Then, the decision support computer program was further developed by adding additional components suggested by the oncology nurses.

Fuzzy logic: A "simple" solution for complexities in neurosciences?

Background:

Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum.

Methods:

This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology.

Results:

The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures.

Conclusions:

In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences.

A study of entropy/clarity of genetic sequences using metric spaces and fuzzy sets

The study of genetic sequences is of great importance in biology and medicine. Sequence analysis and taxonomy are two major fields of application of bioinformatics. In the present paper we extend the notion of entropy and clarity to the use of different metrics and apply them in the case of the Fuzzy Polynuclotide Space (FPS). Applications of these notions on selected polynucleotides and complete genomes both in the I(12×k) space, but also using their representation in FPS are presented. Our results show that the values of fuzzy entropy/clarity are indicative of the degree of complexity necessary for the description of the polynucleotides in the FPS, although in the latter case the interpretation is slightly different than in the case of the I(12×k) hypercube. Fuzzy entropy/clarity along with the use of appropriate metrics can contribute to sequence analysis and taxonomy.

The difference between a dynamic and mechanical approach to stroke treatment

The current classification of stroke is based on causation, also called pathogenesis, and relies on binary logic faithful to the Aristotelian tradition. Accordingly, a pathology is or is not the cause of the stroke, is considered independent of others, and is the target for treatment. It is the subject for large double-blind randomized clinical therapeutic trials. The scientific view behind clinical trials is the fundamental concept that information is statistical, and causation is determined by probabilities. Therefore, the cause and effect relation will be determined by probability-theory-based statistics. This is the basis of evidence-based medicine, which calls for the results of such trials to be the basis for physician decisions regarding diagnosis and treatment. However, there are problems with the methodology behind evidence-based medicine. Calculations using probability-theory-based statistics regarding cause and effect are performed within an automatic system where there are known inputs and outputs. This method of research provides a framework of certainty with no surprise elements or outcomes. However, it is not a system or method that will come up with previously unknown variables, concepts, or universal principles; it is not a method that will give a new outcome; and it is not a method that allows for creativity, expertise, or new insight for problem solving.

Neural Networks for Ischemic Stroke

BACKGROUND

To have uniform criteria for evaluating populations for prevalence of transient ischemic attack (TIA)/stroke, validated instruments are necessary for objective assessment and classification.

METHODS

Patient responses compatible with symptoms of TIA or ischemic stroke, obtained from participants in a substudy of the Asymptomatic Carotid Atherosclerosis Study, were used to program a neural network for each symptom. Models were designed for rapid classification into 1 of 7 outputs: no event, TIA, or stroke (in left carotid, right carotid, or vertebrobasilar). The networks were then tested by comparing decisions with a validated questionnaire used to access an independent data set of 381 patients.

RESULTS

There were 144 patients who reported sudden speech change, 89 with sudden vision loss, 67 with double vision, 189 with sudden numbness, 223 with episodic dizziness, and 108 with paralysis, for a total of 820 reported symptoms among the 381 patients tested. For each category, an equal number of individuals reporting "No" to these phenomena were randomly selected and analyzed. Neural network classification correlated with the diagnoses made by specially trained stroke clinicians (e.g., all who responded "No" were correctly classified as having no neurologic event). Ten symptomatic patients were misclassified, with the most common reason being incomplete data. After adjustment of the network logic, these misclassifications did not recur.

CONCLUSION

Computer networks can be trained to produce a rapid and accurate classification of TIA or stroke by vascular distribution, enabling screening of populations for assessment of their incidence and prevalence.

Fuzzy polynucleotide spaces and metrics

The study of genetic sequences is of great importance in biology and medicine. Mathematics is playing an important role in the study of genetic sequences and, generally, in bioinformatics. In this paper, we extend the work concerning the Fuzzy Polynucleotide Space (FPS) introduced in Torres, A., Nieto, J.J., 2003. The fuzzy polynucleotide Space: Basic properties. Bioinformatics 19(5); 587-592 and Nieto, J.J., Torres, A., Vazquez-Trasande, M.M. 2003. A metric space to study differences between polynucleotides. Appl. Math. Lett. 27:1289-1294: by studying distances between nucleotides and some complete genomes using several metrics. We also present new results concerning the notions of similarity, difference and equality between polynucleotides. The results are encouraging since they demonstrate how the notions of distance and similarity between polynucleotides in the FPS can be employed in the analysis of genetic material.

Thrombophilia in ischemic stroke subtypes: implications for treatment

The current understanding of thrombogenesis is modeled on Virchow's triad: stasis, hypercoagulability, and vessel wall injury. There is a dynamic (always changing) nonlinear interaction between the vascular wall, blood components, and flow, which at times defined "pathologic" leads to thrombosis or hemorrhage, at other times called "healthy" to normal hemostasis. The triad named after Virchow was not designated as such in Virchow's work. Instead, Virchow showed that thrombosis itself leads to endothelial damage, hypercoagulability, and stasis. Thus, cause and effect regarding the elements of Virchow's triad and thrombosis become indistinguishable if linearity is considered mandatory. Considering a nonlinear relation solves this problem. In the real patient, each element is present to a degree. At every moment in time, the direction of coagulation (toward hemostasis, thrombosis, or hemorrhage) and the dynamic of interaction of the elements of the triad change. The complexity and nonlinearity of the thrombotic context is evident. These facts suggest a new venue for diagnostic classification of stroke (ischemic and hemorrhagic) by causation and have implications for its prevention and treatment. Clinical and laboratory evidence can be gathered for the elements of Virchow's triad as well as for fibrinolysis and thrombosis. Mathematical methods other than probability-based statistics can represent the measured presence of these elements to a degree and their nonlinear relationship. These include, but may not be limited to, Riemannian geometry, fuzzy logic, cellular automata, and infinitesimals, all proscribed by evidence-based medicine. However, by using these methods, diagnosis and treatment measures for stroke can be built on a causal rather than risk methodology, individualizing medical decisions to the patient. All current clinical guidelines are based on linear methods of probability-based statistics and group-based data. The therapeutic choice of antithrombotic therapy in the individual patient for whom measured elements of thrombogenesis are available rests on the knowledge and expertise of the treating physician.

Fuzzy logic in medicine and bioinformatics

The purpose of this paper is to present a general view of the current applications of fuzzy logic in medicine and bioinformatics. We particularly review the medical literature using fuzzy logic. We then recall the geometrical interpretation of fuzzy sets as points in a fuzzy hypercube and present two concrete illustrations in medicine (drug addictions) and in bioinformatics (comparison of genomes).

The application of fuzzy logic to the prescription of antithrombotic agents in the elderly

The prescription of antithrombotic agents in the elderly depends, to certain degree, on the identity of the unique individual elderly patient. This dependence cannot be captured by viewing the patient as belonging to a group, but rather by viewing age in the context of unique individual biology. This context is historical, physiological, psychological, and time- and location-dependent. The group-based approach to patient therapy is found in evidence-based medicine, typified by the large double-blind randomised clinical trial from which clinical recommendations are defined. An alternative approach to capturing the unique context of each patient is based using fuzzy logic and mathematics. In particular, the fuzzy subsethood theorem of Kosko has direct application here. A new measure of clinical efficiency, K, derived from the fuzzy subsethood theorem has redefined the clinical significance of age. In particular, the causal role of age in any one patient's response to therapy is to unique degree for that patient. This is because the causal measure K accounts for the role of known and unknown contextual factors of the patient, most of which are unknown, in defining clinical effect in any specific patient. Thus, we have shown mathematically why 'age', or being 'elderly', is only one factor to be taken into consideration when therapeutic decisions regarding antithrombotic therapy are made in any given patient. This is contrary to the group-based approach of evidence-based medicine, where age has the same therapeutic significance for all patients. This is because any hypothesis of evidence-based medicine is group-based and cannot be extrapolated to the individual patient. Such extrapolation has to be personalized through the expertise of the physician. The physician takes into account all the factors not considered in any therapeutic group-based trial which apply to his specific patient. These considerations take into account past experience with that patient. An example of the effect of age on the patient and his/her therapeutic context is provided which shows how age affects different patients to different degree using the fuzzy causal measure K. The purpose of this exercise is to show that there is mathematical support for the argument that individualization of patient therapy by expert decision has measurable clinical significance. What is different here is that measure stick is not probabilistic but fuzzy-mathematic based.

Fuzzy epidemics

The purpose of this paper is to provide a review of the current state of fuzzy logic theory in epidemiology, which is a recent area of research. We present four applications of fuzzy logic theory in epidemic problems, using linguistic fuzzy models, possibility measure, probability of fuzzy events and fuzzy decision making techniques. The results demonstrate that the applications of fuzzy sets in epidemiology is a very promising area of research. The final discussion sets the future stage of fuzzy sets application in epidemiology.

Fuzzy logic and nursing

In empiricism, there are only two answers for a question: black or white. Yet, subjective meanings of human behaviours and responses toward health and illness cannot be simply explained with black and white. Gray zones are needed because they are characterized by complexity and require a contextual understanding. In this paper, we present and suggest fuzzy logic as an example of theoretical bases that help transcend the conflicts between objectivity and subjectivity, respect gray zones between black and white answers for questions, and provide a contextual understanding of complex nursing phenomenon. A historical review of fuzzy logic is followed by a definition of fuzzy logic. Then, fuzzy logic is discussed in terms of its compatibility with nursing epistemological views and philosophical thoughts. Fuzzy logic agrees with three categories of epistemological views of nursing, including correspondence, coherence and pragmatism. Fuzzy logic also agrees with four major philosophical thoughts in nursing, including postempiricism, pragmatism, feminism, and postmodernism. Based on the discussion, we propose that fuzzy logic be further explored, used and developed in research and practice in the nursing areas/situations/phenomena that are characterized by complexity, ambiguousness, and vagueness.

Midpoints for fuzzy sets and their application in medicine

Using Kosko's hypercube, we identify a fuzzy set with a point in a unit hypercube. A non-fuzzy or crisp subset of a set is a vertex of the hypercube. We introduce some new ideas: the definition of the fuzzy segment joining two given fuzzy subsets of a set, the set of midpoints between those two fuzzy subsets, and the set of equidistant points from given points. We present some basic properties and relations between these concepts and provide a complete description of fuzzy segments and midpoints. In the majority of cases, there is no unique midpoint; one has an infinite set of possibilities to choose from. This situation is totally different from classical Euclidean geometry where, for two given points, there is a unique midpoint. We use the obtained results to study two sets of medical data and present two applications in medicine: the fuzzy degree of two concurrent food and drug addictions, and a fuzzy representation of concomitant causal mechanisms of stroke.