11,642 research outputs found
A Decision Technology System To Advance the Diagnosis and Treatment of Breast Cancer
Geographical variations in cancer rates have been observed for decades. Described spatial patterns and trends have provided clues for generating hypotheses about the etiology of cancer. For breast cancer, investigators have demonstrated that some variation can be explained by differences in the population distribution of known breast cancer risk factors such as menstrual and reproductive variables (Laden, Spiegelman, and Neas, 1997; Robbins, Bescianini, and Kelsey, 1997; Sturgeon, Schairer, and Gail, 1995). However, regional patterns also may reflect the effects of Workshop on Hormones, Hormone Metabolism, Environment, and Breast Cancer (1995): (a) environmental hazards (such as air and water pollution), (b) demographics and the lifestyle of a mobile population, (c) subgroup susceptibility, (d) changes and advances in medical practice and healthcare management, and (e) other factors. To accurately measure breast cancer risk in individuals and population groups, it is necessary to singly and jointly assess the association between such risk and the hypothesized factors. Various statistical models will be needed to determine the potential relationships between breast cancer development and estimated exposures to environmental contamination. To apply the models, data must be assembled from a variety of sources, converted into the statistical modelsâ parameters, and delivered effectively to researchers and policy makers. A Web-enabled decision technology system can be developed to provide the needed functionality. This chapter will present a conceptual architecture for such a decision technology system. First, there will be a brief overview of a typical geographical analysis. Next, the chapter will present the conceptual Web-based decision technology system and illustrate how the system can assist users in diagnosing and treating breast cancer. The chapter will conclude with an examination of the potential benefits from system use and the implications for breast cancer research and practice
BCAS: A Web-enabled and GIS-based Decision Support System for the Diagnosis and Treatment of Breast Cancer
For decades, geographical variations in cancer rates have been observed but the precise determinants of such geographic differences in breast cancer development are unclear. Various statistical models have been proposed. Applications of these models, however, require that the data be assembled from a variety of sources, converted into the statistical modelsâ parameters and delivered effectively to researchers and policy makers. A web-enabled and GIS-based system can be developed to provide the needed functionality. This article overviews the conceptual web-enabled and GIS-based system (BCAS), illustrates the systemâs use in diagnosing and treating breast cancer and examines the potential benefits and implications for breast cancer research and practice
Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare
For the last years, time-series mining has become a challenging issue for
researchers. An important application lies in most monitoring purposes, which
require analyzing large sets of time-series for learning usual patterns. Any
deviation from this learned profile is then considered as an unexpected
situation. Moreover, complex applications may involve the temporal study of
several heterogeneous parameters. In that paper, we propose a method for mining
heterogeneous multivariate time-series for learning meaningful patterns. The
proposed approach allows for mixed time-series -- containing both pattern and
non-pattern data -- such as for imprecise matches, outliers, stretching and
global translating of patterns instances in time. We present the early results
of our approach in the context of monitoring the health status of a person at
home. The purpose is to build a behavioral profile of a person by analyzing the
time variations of several quantitative or qualitative parameters recorded
through a provision of sensors installed in the home
An Intelligent Data Mining System to Detect Health Care Fraud
The chapter begins with an overview of the types of healthcare fraud. Next, there is a brief discussion of issues with the current fraud detection approaches. The chapter then develops information technology based approaches and illustrates how these technologies can improve current practice. Finally, there is a summary of the major findings and the implications for healthcare practice
An automated identification and analysis of ontological terms in gastrointestinal diseases and nutrition-related literature provides useful insights
With an unprecedented growth in the biomedical literature, keeping up to date with
the new developments presents an immense challenge. Publications are often studied
in isolation of the established literature, with interpretation being subjective and
often introducing human bias. With ontology-driven annotation of biomedical data
gaining popularity in recent years and online databases offering metatags with rich
textual information, it is now possible to automatically text-mine ontological terms
and complement the laborious task of manual management, interpretation, and
analysis of the accumulated literature with downstream statistical analysis. In this
paper, we have formulated an automated workflow through which we have identified
ontological information, including nutrition-related terms in PubMed abstracts
(from 1991 to 2016) for two main types of Inflammatory Bowel Diseases: Crohnâs
Disease and Ulcerative Colitis; and two other gastrointestinal (GI) diseases, namely,
Coeliac Disease and Irritable Bowel Syndrome. Our analysis reveals unique clustering
patterns as well as spatial and temporal trends inherent to the considered GI diseases
in terms of literature that has been accumulated so far. Although automated
interpretation cannot replace human judgement, the developed workflow shows
promising results and can be a useful tool in systematic literature reviews. The
workflow is available at https://github.com/KociOrges/pytag
Integrating public datasets using linked data: challenges and design principles
The world is moving from a state where there is paucity of data to one of surfeit. These data, and datasets, are normally in different datastores and of different formats. Connecting these datasets together will increase their value and help discover interesting relationships amongst them. This paper describes our experience of using Linked Data to inter-operate these different datasets, the challenges we faced, and the solutions we devised. The paper concludes with apposite design principles for using linked data to inter-operate disparate datasets
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Semantics-Space-Time Cube. A Conceptual Framework for Systematic Analysis of Texts in Space and Time
We propose an approach to analyzing data in which texts are associated with spatial and temporal references with the aim to understand how the text semantics vary over space and time. To represent the semantics, we apply probabilistic topic modeling. After extracting a set of topics and representing the texts by vectors of topic weights, we aggregate the data into a data cube with the dimensions corresponding to the set of topics, the set of spatial locations (e.g., regions), and the time divided into suitable intervals according to the scale of the planned analysis. Each cube cell corresponds to a combination (topic, location, time interval) and contains aggregate measures characterizing the subset of the texts concerning this topic and having the spatial and temporal references within these location and interval. Based on this structure, we systematically describe the space of analysis tasks on exploring the interrelationships among the three heterogeneous information facets, semantics, space, and time. We introduce the operations of projecting and slicing the cube, which are used to decompose complex tasks into simpler subtasks. We then present a design of a visual analytics system intended to support these subtasks. To reduce the complexity of the user interface, we apply the principles of structural, visual, and operational uniformity while respecting the specific properties of each facet. The aggregated data are represented in three parallel views corresponding to the three facets and providing different complementary perspectives on the data. The views have similar look-and-feel to the extent allowed by the facet specifics. Uniform interactive operations applicable to any view support establishing links between the facets. The uniformity principle is also applied in supporting the projecting and slicing operations on the data cube. We evaluate the feasibility and utility of the approach by applying it in two analysis scenarios using geolocated social media data for studying people's reactions to social and natural events of different spatial and temporal scales
Neuroimaging and psychophysiological investigation of the link between anxiety, enhanced affective reactivity and interoception in people with joint hypermobility
Objective: Anxiety is associated with increased physiological reactivity and also increased âinteroceptiveâ sensitivity to such changes in internal bodily arousal. Joint hypermobility, an expression of a common variation in the connective tissue protein collagen, is increasingly recognized as a risk factor to anxiety and related disorders. This study explored the link between anxiety, interoceptive sensitivity and hypermobility in a sub-clinical population using neuroimaging and psychophysiological evaluation.
Methods: Thirty-six healthy volunteers undertook interoceptive sensitivity tests, a clinical examination for hypermobility and completed validated questionnaire measures of state anxiety and body awareness tendency. Nineteen participants also performed an emotional processing paradigm during functional neuroimaging.
Results: We confirmed a significant relationship between state anxiety score and joint hypermobility. Interoceptive sensitivity mediated the relationship between state anxiety and hypermobility. Hypermobile, compared to non-hypermobile, participants displayed heightened neural reactivity to sad and angry scenes within brain regions implicated in anxious feeling states, notably insular cortex.
Conclusions: Our findings highlight the dependence of anxiety state on bodily context, and increase our understanding of the mechanisms through which vulnerability to anxiety disorders arises in people bearing a common variant of collagen
Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
From medical charts to national census, healthcare has traditionally operated
under a paper-based paradigm. However, the past decade has marked a long and
arduous transformation bringing healthcare into the digital age. Ranging from
electronic health records, to digitized imaging and laboratory reports, to
public health datasets, today, healthcare now generates an incredible amount of
digital information. Such a wealth of data presents an exciting opportunity for
integrated machine learning solutions to address problems across multiple
facets of healthcare practice and administration. Unfortunately, the ability to
derive accurate and informative insights requires more than the ability to
execute machine learning models. Rather, a deeper understanding of the data on
which the models are run is imperative for their success. While a significant
effort has been undertaken to develop models able to process the volume of data
obtained during the analysis of millions of digitalized patient records, it is
important to remember that volume represents only one aspect of the data. In
fact, drawing on data from an increasingly diverse set of sources, healthcare
data presents an incredibly complex set of attributes that must be accounted
for throughout the machine learning pipeline. This chapter focuses on
highlighting such challenges, and is broken down into three distinct
components, each representing a phase of the pipeline. We begin with attributes
of the data accounted for during preprocessing, then move to considerations
during model building, and end with challenges to the interpretation of model
output. For each component, we present a discussion around data as it relates
to the healthcare domain and offer insight into the challenges each may impose
on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20
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