Sarcopenia is independently related to hospitalization costs. This condition is estimated to increase hospitalization costs by 58.5% for patients aged <65 years and 34% for patients aged ⩾65 years.

A B S T R A C T ObjectiveTo evaluate the prevalence of undernutrition in older adults aged ≥75 years living in communities and to identify the main factors independently associated with undernutrition. MethodsA cross-sectional study was conducted using a random sample of family physicians' medical records of 86 older adults aged ≥75 years living in the community studied. Their nutritional status was evaluated using the Mini Nutritional Assessment. ResultsA total of 10.5% of the elderly were undernourished and 41.9% were at undernutrition risk. According to the logistic regression multivariable model, the following characteristics: being widowed (OR=6.7; 95%CI=1.8-24.6); being institutionalized (OR=12.6; 95%CI=1.7-90.5); or having a negative self-perception of health (OR=15.0; 95%CI=3.3-69.1) were independently associated with a significant increase of undernutrition risk. ConclusionThe current study shows that undernutrition is highly prevalent in Portuguese older adults aged ≥75 years living in communities. The major factors independently associated with their undernutrition are being widowed and institutionalized and having negative self-perception of health. The results obtained show that undernutrition and its associated factors are very serious problems for older adults and a challenge in their health care.

Abstract-Deep neural networks comprise several hidden layers of units, which can be pre-trained one at a time via an unsupervised greedy approach. A whole network can then be trained (fine-tuned) in a supervised fashion. One possible pre-training strategy is to regard each hidden layer in the network as the input layer of an auto-encoder. Since autoencoders aim to reconstruct their own input, their training must be based on some cost function capable of measuring reconstruction performance. Similarly, the supervised finetuning of a deep network needs to be based on some cost function that reflects prediction performance. In this work we compare different combinations of cost functions in terms of their impact on layer-wise reconstruction performance and on supervised classification performance of deep networks. We employed two classic functions, namely the cross-entropy (CE) cost and the sum of squared errors (SSE), as well as the exponential (EXP) cost, inspired by the error entropy concept. Our results were based on a number of artificial and real-world data sets.

The overall purpose of the research discussed here is the enhancement of home-based care by revealing individual patterns in the life of a person, through modelling of the "busyness" of activity in their dwelling, so that care can be better tailored to their needs and changing circumstances. The use of data mining and on-line analytical processing (OLAP) is potentially interesting in this context because of the possibility of exploring, detecting and predicting changes in the level of activity of people's movement that may reflect change in well-being. An investigation is presented here into the use of data mining and visualisation to illustrate activity from sensor data from a trial project run in a domestic context.