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London : Reproductive Health Matters, 1993-2018.

London : Reproductive Health Matters, 1993-2018.

London : Reproductive Health Matters, 1993-2018.

Application and use of deep learning algorithms for different healthcare applications is gaining interest at a steady pace. However, use of such algorithms can prove to be challenging as they require large amounts of training data that capture different possible variations. This makes it difficult to use them in a clinical setting since in most health applications researchers often have to work with limited data. Less data can cause the deep learning model to over-fit. In this paper, we ask how can we use data from a different environment, different use-case, with widely differing data distributions. We exemplify this use case by using single-sensor accelerometer data from healthy subjects performing activities of daily living - ADLs (source dataset), to extract features relevant to multi-sensor accelerometer gait data (target dataset) for Parkinson's disease classification. We train the pre-trained model using the source dataset and use it as a feature extractor. We show that the features extracted for the target dataset can be used to train an effective classification model. Our pre-trained source model consists of a convolutional autoencoder, and the target classification model is a simple multi-layer perceptron model. We explore two different pre-trained source models, trained using different activity groups, and analyze the influence the choice of pre-trained model has over the task of Parkinson's disease classification.

Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units by means of machine-learning methods. The aim is to support decision making addressed at reducing the incidence rate of infections. In this field, it is necessary to deal with the problem of building reliable classifiers from imbalanced datasets. We propose a clustering-based undersampling strategy to be used in combination with ensemble classifiers. A comparative study with data from 4616 patients was conducted in order to validate our proposal. We applied several single and ensemble classifiers both to the original dataset and to data preprocessed by means of different resampling methods. The results were analyzed by means of classic and recent metrics specifically designed for imbalanced data classification. They revealed that the proposal is more efficient in comparison with other approaches.

Broadcasting Health and Disease: Bodies, markets and television, 1950s–1980s An ERC BodyCapital international conference to be held at the Wellcome Trust, 19–21 February 2018 In the television age, health and the body have been broadcasted in many ways: in short… Continue reading

Surveys provide secure, anonymous feedback from staff at all levels of healthcare organizations

(PRWeb May 06, 2020)

Read the full story at https://www.prweb.com/releases/health_worker_data_alliance_monitoring_emotional_physical_and_occupational_health_of_healthcare_workers_during_covid_19/prweb17101008.htm

Joseph Antonelli, Maitreyi Mazumdar, David Bellinger, David Christiani, Robert Wright, Brent Coull.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 257--275.

Abstract:
Humans are routinely exposed to mixtures of chemical and other environmental factors, making the quantification of health effects associated with environmental mixtures a critical goal for establishing environmental policy sufficiently protective of human health. The quantification of the effects of exposure to an environmental mixture poses several statistical challenges. It is often the case that exposure to multiple pollutants interact with each other to affect an outcome. Further, the exposure-response relationship between an outcome and some exposures, such as some metals, can exhibit complex, nonlinear forms, since some exposures can be beneficial and detrimental at different ranges of exposure. To estimate the health effects of complex mixtures, we propose a flexible Bayesian approach that allows exposures to interact with each other and have nonlinear relationships with the outcome. We induce sparsity using multivariate spike and slab priors to determine which exposures are associated with the outcome and which exposures interact with each other. The proposed approach is interpretable, as we can use the posterior probabilities of inclusion into the model to identify pollutants that interact with each other. We utilize our approach to study the impact of exposure to metals on child neurodevelopment in Bangladesh and find a nonlinear, interactive relationship between arsenic and manganese.

Delta said it is making the move to protect employees amid the coronavirus pandemic, but planes have been flying near empty

Some Buckeyes are not comfortable being told by a "woman in power" to quarantine, one expert said.

China says it will improve public health systems after criticism of its early response to the virus.

Treat the babe in your life to one (or two or three) of these indulgent gifts.

Subhash Kulkarni
Oct 31, 2018; 38:9346-9354
Symposium and Mini-Symposium

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To view more press releases, please visit http://www.snl.com/irweblinkx/news.aspx?iid=1024452.

FAO will use World Food Day this year to promote one of the five pillars of Zero Hunger Challenge. The theme of the campaign will be “Sustainable Food Systems for Food Security and Nutrition.” Events in more than 120 countries – supported by videos, an issues paper, posters, media interviews and more – will communicate the message that our food systems [...]

Have you ever wondered if you are getting adequate nutrients from the food you eat? It is a common misconception that malnutrition means not getting enough food. This is, however, incorrect! People who take in insufficient food can be malnourished, but also those who consume too much face the same risks. Malnutrition is defined as “An abnormal physiological condition caused by [...]