Image data in need of a home.
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ABSTRACT:
SUBMITTER: Lemberger T
PROVIDER: S-EPMC4704486 | biostudies-literature | 2015 Dec
REPOSITORIES: biostudies-literature
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Project description:Recently, the IUCr (International Union of Crystallography) initiated the formation of a Diffraction Data Deposition Working Group with the aim of developing standards for the representation of raw diffraction data associated with the publication of structural papers. Archiving of raw data serves several goals: to improve the record of science, to verify the reproducibility and to allow detailed checks of scientific data, safeguarding against fraud and to allow reanalysis with future improved techniques. A means of studying this issue is to submit exemplar publications with associated raw data and metadata. In a recent study of the binding of cisplatin and carboplatin to histidine in lysozyme crystals under several conditions, the possible effects of the equipment and X-ray diffraction data-processing software on the occupancies and B factors of the bound Pt compounds were compared. Initially, 35.3 GB of data were transferred from Manchester to Utrecht to be processed with EVAL. A detailed description and discussion of the availability of metadata was published in a paper that was linked to a local raw data archive at Utrecht University and also mirrored at the TARDIS raw diffraction data archive in Australia. By making these raw diffraction data sets available with the article, it is possible for the diffraction community to make their own evaluation. This led to one of the authors of XDS (K. Diederichs) to re-integrate the data from crystals that supposedly solely contained bound carboplatin, resulting in the analysis of partially occupied chlorine anomalous electron densities near the Pt-binding sites and the use of several criteria to more carefully assess the diffraction resolution limit. General arguments for archiving raw data, the possibilities of doing so and the requirement of resources are discussed. The problems associated with a partially unknown experimental setup, which preferably should be available as metadata, is discussed. Current thoughts on data compression are summarized, which could be a solution especially for pixel-device data sets with fine slicing that may otherwise present an unmanageable amount of data.
| S-EPMC4187998 | biostudies-literature
Project description:Many bioimage analysis projects produce quantitative descriptors of regions of interest in images. Associating these descriptors with visual characteristics of the objects they describe is a key step in understanding the data at hand. However, as many bioimage data and their analysis workflows are moving to the cloud, addressing interactive data exploration in remote environments has become a pressing issue. To address it, we developed the Image Data Explorer (IDE) as a web application that integrates interactive linked visualization of images and derived data points with exploratory data analysis methods, annotation, classification and feature selection functionalities. The IDE is written in R using the shiny framework. It can be easily deployed on a remote server or on a local computer. The IDE is available at https://git.embl.de/heriche/image-data-explorer and a cloud deployment is accessible at https://shiny-portal.embl.de/shinyapps/app/01_image-data-explorer.
| S-EPMC9477257 | biostudies-literature
Project description:Predicting health-related outcomes can help with proactive healthcare planning and resource management. This is especially important on the older population, an age group growing in the coming decades. Considering longitudinal rather than cross-sectional information from primary care electronic health records (EHRs) can contribute to more informed predictions. In this work, we developed prediction models using longitudinal EHRs to inform resource allocation. In this study, we developed deep-learning-based prognostic models to predict 1-year and 5-year all-cause mortality, nursing home admission, and home care need in people over 65 years old using all the longitudinal information from EHRs. The models included attention mechanisms to increase their transparency. EHRs were drawn from SIDIAP (primary care, Catalonia (Spain)) from 2010-2019. Performance on the test set was compared to that from baseline models using cross-sectional one-year history only. Data from 1,456,052 individuals over 65 years old were considered. Cohen's kappa obtained using longitudinal data was 3.4-fold (1-year all-cause mortality), 10.3-fold (5-year all-cause mortality), 1.1-fold (5-year nursing home admission), and 1.2-fold (5-year home care need) higher than that obtained by the one-year history baseline models. Our models performed better than those not considering longitudinal data, especially when predicting further into the future. However, nursing home admission and home care need in the long term were harder to predict, suggesting their dependence on more abrupt changes. The attention maps helped to understand the predictions, enhancing model transparency. These prediction models can contribute to improve resource allocation in the general population of aging adults.
| S-EPMC11762432 | biostudies-literature
Project description:The aims of the study were to describe (1) the need for help as well as the use and costs of services of home help and/or home nursing (home care) and (2) to identify the variables associated with the use and costs of health and social care services. A total of 721 Finnish home-care clients were interviewed in 2001. The need for help was assessed by basic and instrumental activities of daily Living (ADL) and in terms of pain and illness, rest and sleep, psychosocial well-being and social and environment variables. The Anderson-Newman model was used to study predictors of use of services, including visits of home-care personnel and visits to the doctor, nurse, physiotherapist, laboratory and hospital. Weekly costs of services were calculated. Data were analyzed using multivariate analyses. The clients had poor functional ability and they needed help at least once a week with, on average, 6 out of 15 ADL functions, and 5 out of 13 items relating to pain and illnesses, rest and sleep, psychosocial well-being and social and environment items. The enabling and need variables, particularly the variables "living alone" and "perceived need for help", were important predictors for the use of services. Social care constituted more than half of the average weekly costs of municipalities. The perceived need for help with basic ADL was associated with higher costs. To ensure the quality of life among home-care clients while keeping costs reasonable is a challenge for municipalities.
| S-EPMC5546270 | biostudies-other
Project description:BackgroundJapan faces the most elderly society in the world, and the Japanese government has launched an unprecedented health plan to reinforce home care medicine and increase the number of home care physicians, which means that an understanding of future needs for geriatric home care is vital. However, little is known about the future need for home care physicians. We attempted to estimate the basic need for home care physicians from 2020 to 2060.MethodsOur estimation is based on modification of major health work force analysis methods using previously reported official data. Two models were developed to estimate the necessary number of full-time equivalent (FTE) home care physicians: one based on home care patient mortality, the other using physician-to-patient ratio, working with estimated numbers of home and nursing home deaths from 2020 to 2060. Moreover, the final process considered and adjusted for future changes in the proportion of patients dying at home. Lastly, we converted estimated FTE physicians to an estimated head count.ResultsResults were concordant between our two models. In every instance, there was overlap of high- and low-estimations between the mortality method and the physician-to-patient method, and the estimates show highly similar patterns. Furthermore, our estimation is supported by the current number of physicians, which was calculated using a different method. Approximately 1.7 times (1.6 by head count) the current number of FTE home care physicians will be needed in Japan in the late 2030's, peaking at 33,500 FTE (71,500 head count). However, the need for home care physicians is anticipated to begin decreasing by 2040.ConclusionThe results indicate that the importance of home care physicians will rise with the growing elderly population, and that improvements in home care could partially suppress future need for physicians. After the late 2030's, the supply can be reduced gradually, accounting for the decreasing total number of deaths after 2040. In order to provide sufficient home care and terminal care at home, increasing the number of home care physicians is indispensable. However, the unregulated supply of home care physicians will require careful attention in the future.
| S-EPMC7429680 | biostudies-literature
Project description:In underwater environment, the study of object recognition is an important basis for implementing an underwater unmanned vessel. For this purpose, abundant experimental data to train deep learning model is required. However, it is very difficult to obtain these data because the underwater experiment itself is very limited in terms of preparation time and resources. In this study, the image transformation model, Pix2Pix is utilized to generate data similar to experimental one obtained by our ROV named SPARUS between the pool and reservoir. These generated data are applied to train the other deep learning model, FCN for a pixel segmentation of images. The original sonar image and its mask image have to be prepared for all training data to train the image segmentation model and it takes a lot of effort to do it what if all training data are supposed to be real sonar images. Fortunately, this burden can be released here, for the pairs of mask image and synthesized sonar image are already consisted in the image transformation step. The validity of the proposed procedures is verified from the performance of the image segmentation result. In this study, when only real sonar images are used for training, the mean accuracy is 0.7525 and the mean IoU is 0.7275. When the both synthetic and real data is used for training, the mean accuracy is 0.81 and the mean IoU is 0.7225. Comparing the results, the performance of mean accuracy increase to 6%, performance of the mean IoU is similar value.
| S-EPMC9374219 | biostudies-literature
Project description:Data classification is one of the most commonly used applications of machine learning. The are many developed algorithms that can work in various environments and for different data distributions that perform this task with excellence. Classification algorithms, just like other machine learning algorithms have one thing in common: in order to operate on data, they must see the data. In the present world, where concerns about privacy, GDPR (General Data Protection Regulation), business confidentiality and security are growing bigger and bigger; this requirement to work directly on the original data might become, in some situations, a burden. In this paper, an approach to the classification of images that cannot be directly accessed during training has been made. It has been shown that one can train a deep neural network to create such a representation of the original data that i) without additional information, the original data cannot be restored, and ii) that this representation-called a masked form-can still be used for classification purposes. Moreover, it has been shown that classification of the masked data can be done using both classical and neural network-based classifiers.
| S-EPMC8259988 | biostudies-literature
Project description:Spondyloarthritis comprises a group of inflammatory diseases, characterised by inflammation within axial joints and/or peripheral arthritis, enthesitis and dactylitis. An increasing number of biologic treatments, including biosimilars, are available for the treatment of spondyloarthritis. Although there are a growing number of randomised controlled trials assessing treatments in spondyloarthritis, there is a paucity of data from head-to-head studies. Comparative data are required so that clinicians and payers have the level of evidence required to inform clinical decision-making and health economic assessments. In the absence of head-to-head studies, statistical methods such as network meta-analyses and matching-adjusted indirect comparisons (MAICs) are used for assessing comparative effectiveness.Network meta-analysis can be used to compare treatments for trials using a common comparator (e.g. placebo); however, for those without a common comparator or where considerable heterogeneity exists between the study populations, a MAIC that controls for differences in study design and baseline patient characteristics may be used. MAICs, unlike network meta-analyses, are of value for longer-term comparisons beyond the placebo-controlled phase of clinical trials, which is important for chronic diseases requiring long-term treatment, like spondyloarthritis. At present, there are a number of limitations that restrict the effectiveness of MAIC, such as the poor availability of individual patient-level data from trials, which results in patient-level data from one trial being compared with published whole-population data from another. Despite these limitations, drug reimbursement agencies are increasingly accepting MAIC as a means of comparative effectiveness and greater methodological guidance is needed.This report highlights a number of challenges that are specific to conducting comparative studies like MAIC in spondyloarthritis, including disease heterogeneity, the paucity of biomarkers and the duration of studies required for radiographic endpoints in this slow-progressing disease.
| S-EPMC6341745 | biostudies-literature
Project description:PurposeThe patient-centered medical home (PCMH) has emerged as an optimal primary care model for all youth; however, little is known about the extent to which adolescents in need of mental health (MH) treatment receive care consistent with the PCMH. This study assessed (1) 10-year trends in PCMH care among U.S. adolescents according to MH need and (2) variations in PCMH care and its subcomponents among adolescents with MH need, by individual and family characteristics.MethodsThis was a secondary analysis of Medical Expenditure Panel Survey data (2004-2013). The sample included adolescents aged 12-17 years with ≥1 office-based visits in the past year (N = 18,717). Questions assessing a usual source of care and care that is accessible, comprehensive, family-centered, and compassionate were used to define PCMH care. For adolescents with MH needs, multivariable logistic regression was used to describe the association between PCMH care and sample characteristics.ResultsFifty percent of adolescents experienced PCMH care, with little change between 2004 and 2013. Adolescents with MH need (N = 3,794) had significantly lower odds of experiencing PCMH care compared with those without MH need (odds ratio, .78; 95% confidence interval, .69-.87). Among adolescents with MH needs, being uninsured and living with a parent who did not graduate high school were negatively associated with PCMH care, whereas parental usual source of care was positively associated (odds ratio, 1.69; 95% confidence interval, 1.28-2.22).ConclusionsIncreasing care accessibility, integrating MH services into primary care settings, and targeting socioeconomically disadvantaged subgroups could improve rates of PCMH care among adolescents with MH needs.
| S-EPMC6113081 | biostudies-literature
Project description: Not available
| S-EPMC1420761 | biostudies-literature