Fuzzy neural network methodology applied to medical diagnosis

This paper presents a technique for building expert systems that combines the fuzzy-set approach with artificial neural network structures. This technique can effectively deal with two types of medical knowledge: a nonfuzzy one and a fuzzy one which usually contributes to the process of medical diagnosis. Nonfuzzy numerical data is obtained from medical tests. Fuzzy linguistic rules describing the diagnosis process are provided by a human expert. The proposed method has been successfully applied in veterinary medicine as a support system in the diagnosis of canine liver diseases

A neuro-fuzzy approach as medical diagnostic interface

In contrast to the symbolic approach, neural networks seldom are designed to explain what they have learned. This is a major obstacle for its use in everyday life. With the appearance of neuro-fuzzy systems which use vague, human-like categories the situation has changed. Based on the well-known mechanisms of learning for RBF networks, a special neuro-fuzzy interface is proposed in this paper. It is especially useful in medical applications, using the notation and habits of physicians and other medically trained people. As an example, a liver disease diagnosis system is presented

A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition

Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed

Beyond the clinic? Eluding a medical diagnosis of anorexia through narrative

The persistence and recurrence of anorexia nervosa poses a clinical challenge, and provides support for critiques of oppressive and injurious facets of society inscribed on women’s bodies. This essay illustrates how a phenomenological, linguistic anthropological approach fruitfully traverses clinical and cultural perspectives by directing attention beyond the embodied experience of patients diagnosed with anorexia nervosa to those who are not clinically diagnosed. Extending a model of illness and recovery as entailing sufferers’ emplotting of past, present, and imagined future selves, I argue that women’s accounts of their experiences do not simply reflect lived reality, but actually propel health-relevant states of being by enlivening and creating these realities in the process of their telling. In indexical interaction with public and clinical discourses, narratives’ grammar, lexicon, and plot structures modify subjects’ experiences and interpretations of the events and feelings recounted. This article builds on the insight that linear narratives of “full recovery” that adopt a clinical and feminist voice can help tellers stay recovered, whereas for those “struggling to recover,” a genre of contingent, uncertain, sideshadowing narratives alternatively renders recovery an elusive and ambivalently desired object. This essay then identifies a third narrative genre, eluding a diagnosis, which combines elements of the first two genres to paradoxically keep its teller simultaneously sheltered from, and invisible to the well-meaning clutches of medical care, leaving her suffering, yet free, to starve. This focus on narrative genres illustrates the utility of linguistic analyses for discerning and interpreting distress in subclinical populations.First author draf

Fuzzy logic as a decision-making support system for the indication of bariatric surgery based on an index (OBESINDEX) generated by the association between body fat and body mass index

Background: A Fuzzy Obesity Index (OBESINDEX) for use as an alternative in bariatric surgery indication (BSI) is presented. The search for a more accurate method to evaluate obesity and to indicate a better treatment is important in the world health context. BMI (body mass index) is considered the main criteria for obesity treatment and BSI. Nevertheless, the fat excess related to the percentage of Body Fat (%BF) is actually the principal harmful factor in obesity disease that is usually neglected. This paper presents a new fuzzy mechanism for evaluating obesity by associating BMI with %BF that yields a fuzzy obesity index for obesity evaluation and treatment and allows building up a Fuzzy Decision Support System (FDSS) for BSI.

Methods: Seventy-two patients were evaluated for both BMI and %BF. These data are modified and treated as fuzzy sets. Afterwards, the BMI and %BF classes are aggregated yielding a new index (OBESINDEX) for input linguistic variable are considered the BMI and %BF, and as output linguistic variable is employed the OBESINDEX, an obesity classification with entirely new classes of obesity in the fuzzy context as well is used for BSI.

Results: There is a gradual, smooth obesity classification and BSI when using the proposed fuzzy obesity index when compared with other traditional methods for dealing with obesity.

Conclusion: The BMI is not adequate for surgical indication in all the conditions and fuzzy logic becomes an alternative for decision making in bariatric surgery indication based on the OBESINDEX

\u27Death is difficult in any language\u27: A qualitative study of palliative care professionals\u27 experiences when providing end-of-life care to patients from culturally and linguistically diverse backgrounds

Background: Ethnic minority patients have unique challenges in accessing health services. These include language difficulties, unfamiliarity with the health system, lower rates of cancer screening and survival, higher rates of reported side effects from cancer treatment and poorer quality of life. Little is known about this patient group when transitioning to palliative care. Aim: To elicit the experiences of palliative care health professionals when providing care for patients from culturally and linguistically diverse backgrounds which differ from mainstream Australian language and culture. Design: An emergent qualitative design, informed by theoretical and procedural direction from grounded theory research. Setting/participants: Four focus groups held with palliative care staff (n=28) in a single specialist palliative care service in Australia. Results: The following themes emerged: 1) determining the rules of engagement around discussion of diagnosis and prognosis; 2) navigating the challenge of language to patient understanding; 3) understanding migration experiences to establish trust; 4) maintaining the balance between patient safety and comfort care; 5) providing a good death experience through accommodation of beliefs; and 6) navigating the important role of family members while privileging patient preferences. Conclusion: Underlying provider perceptions of caring for patients was that death is difficult in any language. Care was conceptualised as considering cultural and linguistic backgrounds within individualistic care. Understanding the migration experience and building trust were key elements of this individualised approach. Acknowledgement of the key role played by families in patient care and safety are strategies to minimise barriers and understand the concerns of this patient group

Fuzzy logic as a decision-making support system for the indication of bariatric surgery based on an index (MAFOI) generated by the association between body fat and body mass index.

Background: A fuzzy obesity index (MAFOI) for use as an alternative to bariatric surgery indication (BSI) is presented. The search for a more accurate method to evaluate obesity and to indicate a better treatment is important in the world health context. BMI (body mass index) is considered the main criteria for obesity treatment and BSI. Nevertheless, the fat excess related to the percentage of Body Fat (%BF) is actually the principal harmful factor in obesity disease that is usually neglected. This paper presents a new fuzzy mechanism for evaluating obesity by associating BMI with %BF that yields a fuzzy obesity index for obesity evaluation and treatment and allows building up a Fuzzy Decision Support System (FDSS) for BSI. Methods: Seventy-two patients were evaluated for both BMI and %BF. These data are modified and treated as fuzzy sets. Afterwards, the BMI and %BF classes are aggregated yielding a new index (MAFOI) for input linguistic variable are considered the BMI and %BF, and as output linguistic variable is employed the MAFOI, an obesity classification with entirely new classes of obesity in the fuzzy context as well as is used for BSI. Results: There is gradual, smooth obesity classification and BSI when using the proposed fuzzy obesity index when compared with other traditional methods for dealing with obesity.
Conclusion: The BMI is not adequate for surgical indication in all the conditions and fuzzy logic becomes an alternative for decision making in bariatric surgery indication based on the MAFOI