25,279 research outputs found
DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways
Clinical researchers use disease progression models to understand patient
status and characterize progression patterns from longitudinal health records.
One approach for disease progression modeling is to describe patient status
using a small number of states that represent distinctive distributions over a
set of observed measures. Hidden Markov models (HMMs) and its variants are a
class of models that both discover these states and make inferences of health
states for patients. Despite the advantages of using the algorithms for
discovering interesting patterns, it still remains challenging for medical
experts to interpret model outputs, understand complex modeling parameters, and
clinically make sense of the patterns. To tackle these problems, we conducted a
design study with clinical scientists, statisticians, and visualization
experts, with the goal to investigate disease progression pathways of chronic
diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's
disease, and chronic obstructive pulmonary disease (COPD). As a result, we
introduce DPVis which seamlessly integrates model parameters and outcomes of
HMMs into interpretable and interactive visualizations. In this study, we
demonstrate that DPVis is successful in evaluating disease progression models,
visually summarizing disease states, interactively exploring disease
progression patterns, and building, analyzing, and comparing clinically
relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic
Information visualization for DNA microarray data analysis: A critical review
Graphical representation may provide effective means of making sense of the complexity and sheer volume of data produced by DNA microarray experiments that monitor the expression patterns of thousands of genes simultaneously. The ability to use ldquoabstractrdquo graphical representation to draw attention to areas of interest, and more in-depth visualizations to answer focused questions, would enable biologists to move from a large amount of data to particular records they are interested in, and therefore, gain deeper insights in understanding the microarray experiment results. This paper starts by providing some background knowledge of microarray experiments, and then, explains how graphical representation can be applied in general to this problem domain, followed by exploring the role of visualization in gene expression data analysis. Having set the problem scene, the paper then examines various multivariate data visualization techniques that have been applied to microarray data analysis. These techniques are critically reviewed so that the strengths and weaknesses of each technique can be tabulated. Finally, several key problem areas as well as possible solutions to them are discussed as being a source for future work
Exploring Causal Influences
Recent data mining techniques exploit patterns of statistical independence in multivariate data to make conjectures about cause/effect relationships. These relationships can be used to construct causal graphs, which are sometimes represented by weighted node-link diagrams, with nodes representing variables and combinations of weighted links and/or nodes showing the strength of causal relationships. We present an interactive visualization for causal graphs (ICGs), inspired in part by the Influence Explorer. The key principles of this visualization are as follows: Variables are represented with vertical bars attached to nodes in a graph. Direct manipulation of variables is achieved by sliding a variable value up and down, which reveals causality by producing instantaneous change in causally and/or probabilistically linked variables. This direct manipulation technique gives users the impression they are causally influencing the variables linked to the one they are manipulating. In this context, we demonstrate the subtle distinction between seeing and setting of variable values, and in an extended example, show how this visualization can help a user understand the relationships in a large variable set, and with some intuitions about the domain and a few basic concepts, quickly detect bugs in causal models constructed from these data mining techniques
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
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