Are There Differences in the Prevalence of Palliative Care-Related Problems in People Living With Advanced Cancer and Eight Non-Cancer Conditions? A Systematic Review

AbstractContextIf access to effective palliative care is to extend beyond cancer patients, an understanding of the comparative prevalence of palliative care problems among cancer and non-cancer patients is necessary.ObjectivesThis systematic review aimed to describe and compare the prevalence of seventeen palliative care-related problems across the four palliative care domains among adults with advanced cancer, acquired immune deficiency syndrome, chronic heart failure, end-stage renal disease (ESRD), chronic obstructive pulmonary disease, multiple sclerosis, motor neuron disease, Parkinson's disease, and dementia.MethodsThree databases were searched using three groups of keywords. The results of the extraction of the prevalence figures were summarized.ResultsThe electronic searches yielded 4697 hits after the removal of 1784 duplicates. Of these hits, 143 met the review criteria. The greatest number of studies were found for advanced cancer (n=57) and ESRD patients (n=47), and 75 of the 143 studies used validated scales. Few data were available for people living with multiple sclerosis (n=2) and motor neuron disease (n=3). The problems with a prevalence of 50% or more found across most of the nine studied diagnostic groups were: pain, fatigue, anorexia, dyspnea, and worry.ConclusionThere are commonalities in the prevalence of problems across cancer and non-cancer patients, highlighting the need for palliative care to be provided irrespective of diagnosis. The methodological heterogeneity across the studies and the lack of non-cancer studies need to be addressed in future research

Learning from the public:citizens describe the need to improve end-of-life care access, provision and recognition across Europe

Background: Despite ageing populations and increasing cancer deaths, many European countries lack national policies regarding palliative and end-of-life care. The aim of our research was to determine public views regarding end-of-life care in the face of serious illness. Methods: Implementation of a pan-European population-based survey with adults in England, Belgium (Flanders), Germany, Italy, the Netherlands, Portugal and Spain. Three stages of analysis were completed on open-ended question data: (i) inductive analysis to determine a category-code framework; (ii) country-level manifest deductive content analysis; and (iii) thematic analysis to identify cross-country prominent themes. Results: Of the 9344 respondents, 1543 (17%) answered the open-ended question. Two prominent themes were revealed: (i) a need for improved quality of end-of-life and palliative care, and access to this care for patients and families and (ii) the recognition of the importance of death and dying, the cessation of treatments to extend life unnecessarily and the need for holistic care to include comfort and support. Conclusions: Within Europe, the public recognizes the importance of death and dying; they are concerned about the prioritization of quantity of life over quality of life; and they call for improved quality of end-of-life and palliative care for patients, especially for elderly patients, and families. To fulfil the urgent need for a policy response and to advance research and care, we suggest four solutions for European palliative and end-of-life care: institute government-led national strategies; protect regional research funding; consider within-and between-country variance; establish standards for training, education and service delivery

Attention-based Fusion for Outfit Recommendation

status: accepte

A Comparative Study of Outfit Recommendation Methods with a Focus on Attention-based Fusion

https://authors.elsevier.com/a/1bI5R15hYdjpsKstatus: Published onlin

Learning Explainable Disentangled Representations of E-Commerce Data by Aligning Their Visual and Textual Attributes

Understanding multimedia content remains a challenging problem in e-commerce search and recommendation applications. It is difficult to obtain item representations that capture the relevant product attributes since these product attributes are fine-grained and scattered across product images with huge visual variations and product descriptions that are noisy and incomplete. In addition, the interpretability and explainability of item representations have become more important in order to make e-commerce applications more intelligible to humans. Multimodal disentangled representation learning, where the independent generative factors of multimodal data are identified and encoded in separate subsets of features in the feature space, is an interesting research area to explore in an e-commerce context given the benefits of the resulting disentangled representations such as generalizability, robustness and interpretability. However, the characteristics of real-word e-commerce data, such as the extensive visual variation, noisy and incomplete product descriptions, and complex cross-modal relations of vision and language, together with the lack of an automatic interpretation method to explain the contents of disentangled representations, means that current approaches for multimodal disentangled representation learning do not suffice for e-commerce data. Therefore, in this work, we design an explainable variational autoencoder framework (E-VAE) which leverages visual and textual item data to obtain disentangled item representations by jointly learning to disentangle the visual item data and to infer a two-level alignment of the visual and textual item data in a multimodal disentangled space. As such, E-VAE tackles the main challenges in disentangling multimodal e-commerce data. Firstly, with the weak supervision of the two-level alignment our E-VAE learns to steer the disentanglement process towards discovering the relevant factors of variations in the multimodal data and to ignore irrelevant visual variations which are abundant in e-commerce data. Secondly, to the best of our knowledge our E-VAE is the first VAE-based framework that has an automatic interpretation mechanism that allows to explain the components of the disentangled item representations with text. With our textual explanations we provide insight in the quality of the disentanglement. Furthermore, we demonstrate that with our explainable disentangled item representations we achieve state-of-the-art outfit recommendation results on the Polyvore Outfits dataset and report new state-of-the-art cross-modal search results on the Amazon Dresses dataset