Uncovering the challenges of managing cachexia in advanced cancer: Preliminary results from semi-structured interviews with healthcare professionals in a regional cancer centre

Background: Cancer cachexia is a complex metabolic syndrome characterised by severe and progressive weight loss which is predominantly muscle mass. It is a devastating and distressing complication of advanced cancer with profound bio-psycho-social implications for patients and their families. At present there is no curative treatment for cachexiain advanced cancer therefore the most important healthcare response entails the minimisation of the psycho-social distress associated with it. However the literature suggests healthcare professionals’are missing opportunities to intervene and respond to the multi-dimensional needs of this population.

Objective:The objective of this study was to explore healthcare professionals’ response to cachexia in advanced cancer.

Methods: An interpretative qualitative approach was adopted in this study. A purposive sample of doctors, nurses, specialist nurses and dieticians were recruited from a regional cancer centre between November 2009 and November 2010. Data was collection was twofold: two multi-professional focus groups were conducted first to uncover the main themes and issues in cachexia management. This data then informed the interview schedule for the following 25 individual semi-structured interviews.

Results: Preliminary data analysis of the semi-structured interviews revealed distinct differences between disciplines in their perceptions of cancer cachexia which influenced their response to it in clinical practice. The commonality between disciplines, with the exception of palliative care, was a reliance on the biomedical approach to cancer cachexia management.

Discussion and Conclusions: Cancer cachexia is a complex and challenging syndrome which needs to be addressed from a holistic model of care to reflect the multi-dimensional needs of this patient group. The perspectives of those involved in care delivery is required in order to inform the development of interventions aimed at minimising the distress associated with this devastating syndrome.

Tangible applications for regular objects:An end-user model for pervasive computing at home

This paper describes an end-user model for a domestic pervasive computing platform formed by regular home objects. The platform does not rely on pre-planned infrastructure; instead, it exploits objects that are already available in the home and exposes their joint sensing, actuating and computing capabilities to home automation applications. We advocate an incremental process of the platform formation and introduce tangible, object-like artifacts for representing important platform functions. One of those artifacts, the application pill, is a tiny object with a minimal user interface, used to carry the application, as well as to start and stop its execution and provide hints about its operational status. We also emphasize streamlining the user's interaction with the platform. The user engages any UI-capable object of his choice to configure applications, while applications issue notifications and alerts exploiting whichever available objects can be used for that purpose. Finally, the paper briefly describes an actual implementation of the presented end-user model. © (2010) by International Academy, Research, and Industry Association (IARIA).

Complexity of Inferences in Polytree-shaped Semi-Qualitative Probabilistic Networks

Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Bayesian networks and qualitative probabilistic networks. They provide a very general modeling framework by allowing the combination of numeric and qualitative assessments over a discrete domain, and can be compactly encoded by exploiting the same factorization of joint probability distributions that are behind the Bayesian networks. This paper explores the computational complexity of semi-qualitative probabilistic networks, and takes the polytree-shaped networks as its main target. We show that the inference problem is coNP-Complete for binary polytrees with multiple observed nodes. We also show that inferences can be performed in linear time if there is a single observed node, which is a relevant practical case. Because our proof is constructive, we obtain an efficient linear time algorithm for SQPNs under such assumptions. To the best of our knowledge, this is the first exact polynomial-time algorithm for SQPNs. Together these results provide a clear picture of the inferential complexity in polytree-shaped SQPNs.

Semi-Qualitative Probabilistic Networks in Computer Vision Problems

This paper explores the application of semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information to computer vision problems. Our version of SQPN allows qualitative influences and imprecise probability measures using intervals. We describe an Imprecise Dirichlet model for parameter learning and an iterative algorithm for evaluating posterior probabilities, maximum a posteriori and most probable explanations. Experiments on facial expression recognition and image segmentation problems are performed using real data.

Belief Updating and Learning in Semi-Qualitative Probabilistic Networks

This paper explores semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information. We first show that exact inferences with SQPNs are NPPP-Complete. We then show that existing qualitative relations in SQPNs (plus probabilistic logic and imprecise assessments) can be dealt effectively through multilinear programming. We then discuss learning: we consider a maximum likelihood method that generates point estimates given a SQPN and empirical data, and we describe a Bayesian-minded method that employs the Imprecise Dirichlet Model to generate set-valued estimates.

On the Merit of Selecting Different Belief Merging Operators

Belief merging operators combine multiple belief bases (a profile) into a collective one. When the conjunction of belief bases is consistent, all the operators agree on the result. However, if the conjunction of belief bases is inconsistent, the results vary between operators. There is no formal manner to measure the results and decide on which operator to select. So, in this paper we propose to evaluate the result of merging operators by using three ordering relations (fairness, satisfaction and strength) over operators for a given profile. Moreover, a relation of conformity over operators is introduced in order to classify how well the operator conforms to the definition of a merging operator. By using the four proposed relations we provide a comparison of some classical merging operators and evaluate the results for some specific profiles.

A comparison of merging operators in possibilistic logic.

In this paper, we compare merging operators in possibilistic logic. We rst propose an approach to evaluating the discriminating power of a merging operator. After that, we analyze the computational complexity of existing possibilistic merging operators. Finally, we consider the compatibility of possibilistic merging operators with propositional merging operators.