An increasing proportion of critically ill patients are elderly (ie, ≥ 65 years of age). This poses complex challenges and choices for the management of elderly patients. Outcome following admission ...to the ICU has been traditionally concerned with mortality. Beyond mortality, outcomes such as functional status and health-related quality of life (HRQOL) have assumed greater importance. This article reviews the literature, published in English from 1990 to December 2003, pertaining to HRQOL and functional status outcomes of elderly patients. Functional status and HRQOL of elderly survivors of ICUs has been underinvestigated. There is no agreement as to the optimal instrument choice, and differences between studies preclude meaningful comparison or pooling of results.
Abstract Background For adults with chronic conditions, access to primary care, including multidisciplinary care, is associated with better outcomes. Few studies have assessed barriers to such care. ...We sought to describe barriers to primary care, including care from allied health professionals, for adults with chronic conditions. Methods We surveyed western Canadians aged 40 years or older who had hypertension, diabetes, heart disease or stroke about access to primary care and other use of health care. Using log binomial regression, we determined the association between sociodemographic variables and several indicators of access to primary care and care from allied health professionals. Results Of the 2316 people who were approached, 1849 (79.8%) completed the survey. Most of the respondents (95.1%) had a regular medical doctor, but two-thirds (68.1%) did not have after-hours access. Only 6.1% indicated that allied health professionals were involved in their care, although most respondents (87.3%) indicated they would be willing to see a nurse practitioner if their primary care physician was not available. Respondents who were obese or less than 65 years of age were less likely to have a regular medical doctor. Individuals who had diabetes, lived in a rural area, were residents of Alberta or had poorer health were more likely to have allied health professionals involved in their care. Interpretation The survey results identified barriers to accessing primary care for people with chronic conditions. Opportunities for improving access to primary care may include greater involvement by allied health professionals, such as nurse practitioners.
Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk production. By accurately ...estimating herbage mass and composition, targeted nitrogen fertiliser application strategies can be deployed to improve localised regions in a herbage field, effectively reducing the negative impacts of over-fertilization on biodiversity and the environment. In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage. The process is labour intensive and time consuming and so not utilised by farmers. Deep learning has been successfully applied in this context on images collected by high-resolution cameras on the ground. Moving the deep learning solution to drone imaging, however, has the potential to further improve the herbage mass yield and composition estimation task by extending the ground-level estimation to the large surfaces occupied by fields/paddocks. Drone images come at the cost of lower resolution views of the fields taken from a high altitude and requires further herbage ground-truth collection from the large surfaces covered by drone images. This paper proposes to transfer knowledge learned on ground-level images to raw drone images in an unsupervised manner. To do so, we use unpaired image style translation to enhance the resolution of drone images by a factor of eight and modify them to appear closer to their ground-level counterparts. We then use the enhanced drone images to train a semi-supervised algorithm that uses ground-truthed, ground-level images as the labelled data together with a large amount of unlabeled drone images. We validate our results on a small held-out drone image test set to show the validity of our approach, which opens the way for automated dry herbage biomass monitoring www.github.com/PaulAlbert31/Clover_SSL.
Monitoring species-specific dry herbage biomass is an important aspect of pasture-based milk production systems. Being aware of the herbage biomass in the field enables farmers to manage surpluses ...and deficits in herbage supply, as well as using targeted nitrogen fertilization when necessary. Deep learning for computer vision is a powerful tool in this context as it can accurately estimate the dry biomass of a herbage parcel using images of the grass canopy taken using a portable device. However, the performance of deep learning comes at the cost of an extensive, and in this case destructive, data gathering process. Since accurate species-specific biomass estimation is labor intensive and destructive for the herbage parcel, we propose in this paper to study low supervision approaches to dry biomass estimation using computer vision. Our contributions include: a synthetic data generation algorithm to generate data for a herbage height aware semantic segmentation task, an automatic process to label data using semantic segmentation maps, and a robust regression network trained to predict dry biomass using approximate biomass labels and a small trusted dataset with gold standard labels. We design our approach on a herbage mass estimation dataset collected in Ireland and also report state-of-the-art results on the publicly released Grass-Clover biomass estimation dataset from Denmark. Our code is available at https://git.io/J0L2a
Sward species composition estimation is a tedious one. Herbage must be collected in the field, manually separated into components, dried and weighed to estimate species composition. Deep learning ...approaches using neural networks have been used in previous work to propose faster and more cost efficient alternatives to this process by estimating the biomass information from a picture of an area of pasture alone. Deep learning approaches have, however, struggled to generalize to distant geographical locations and necessitated further data collection to retrain and perform optimally in different climates. In this work, we enhance the deep learning solution by reducing the need for ground-truthed (GT) images when training the neural network. We demonstrate how unsupervised contrastive learning can be used in the sward composition prediction problem and compare with the state-of-the-art on the publicly available GrassClover dataset collected in Denmark as well as a more recent dataset from Ireland where we tackle herbage mass and height estimation.
PDF
