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FasterAnalytics
for Healthcare - A Diagnostic Case Study
DecisionQ
has developed FasterAnalytics,
a unique analytics package that enables researchers, analysts,
and managers to use sophisticated predictive analytics from the
desktop. FasterAnalytics is fast and creates high quality, predictive
models from data that enable efficient review of clinical data,
real-time hypothesis testing, and rapid decisions.
FasterAnalytics
uses a modeling approach called Bayesian Networks to provide a
mapping of the complex relationships in data, which can then be
used to make high quality predictions. Users can:
- Get
an instant global view of their data.
- Understand
the driving factors in the data.
- Test
hypotheses in real time in our model Explorer.
- Produce
reports that can be exported to other applications.
- Make
determinations that can help prioritize the use of scarce research
resources.
Market
Overview
Clinicians
consistently desire better tools to enhance their diagnosis and
prognosis efforts. The more accurate they can be in identifying
diseases and expected outcomes, the better they can be at designing
appropriate treatment protocols. To assist in this effort, DecisionQ
has developed FasterAnalytics
for Healthcare, a tool for modeling healthcare data. DecisionQ's
FasterAnalytics software can combine data and clinician experience
to create powerful predictive models, models that can be used
to improve patient treatments and outcomes.
Value
to the Customer
FasterAnalytics
enables both experts and non-experts in statistics to discover
and leverage knowledge from large quantities of data quickly.
Examples include:
- Automatically
mapping data where targets are unknown to reveal correlations.
- Discovering
new relationships between variables and identifying new opportunities
to improve care or reduce cost.
- Identifying
potential morbidities early.
- Discovering
populations that may have substantially different responses
from the population at large.
- Predicting
the behavior of any factor or combination of factors in the
model.
FasterAnalytics
is designed for real-time environments. Bayesian models are highly
effective at identifying emerging trends that can be used to either
to identify potential adverse advents or improve quality of outcomes.
Product
and Technology
DecisionQ
Corporation has produced a range of modules that include data
analysis, modeling, visualization, reporting, and decision optimization.
FasterAnalytics modules include:
- Discretizer.
Automatically configures the data for modeling.
- Modeler.
Quickly creates a visual model of the data.
- Explorer.
Allows real-time generation and testing of hypotheses.
- Reporter.
Extracts insights and key points for inclusion in reports and
presentations.
Using
the System: A Diagnostic/Prognostic Example
The
following is an example application of our software to a set of
breast cancer data. We have used a set comprised of 457 breast
cancer patients with 10 attributes or markers. FasterAnalytics
built the model in this example, from start to finish, in less
than 5 minutes.
To
build predictive models, our learning engine requires the data
to be in a flat tabular format. The data can be numerical, or
variable character strings. Our software also handles missing
values automatically and will either impute a value or treat missing
values as a special category, at the user's discretion.
Figure
1: This example uses a data set held in an Excel spreadsheet as
shown below (Partial).
Having
selected the data, a fully automated process will continue until
a full model is presented, or the user can stop each part of the
process to manually change parameters. The software begins by
categorizing the data and 'binning' in accordance with the default
settings. The data is then passed seamlessly to the Modeler for
automated model development. Once the software has mapped the
complex correlations in the data a model is presented in the Explorer.
Figure
2: Base case model of the data presented in Explorer
The
display illustrates conditional dependence between variables and
the pathways existing in the prognostics model. Notice that the
network has multiple branches, and that the data is interrelated
in a "web", one of the strengths of multivariate Bayesian networks.
In
the example below, we examine likely prognostic and predictive
indicators that correlate with breast cancer patient outcome,
and correlate with the subjective finding of nuclear grade of
tumor cells. We begin by selecting our target variable, Nuclear
Grade. We can see that the coloring and distribution of the surrounding
nodes has changed to indicate the effect on other prognostic markers
associated with the current case. Note that Estrogen Expression
has decreased dramatically, HER2 Expression has increased, and
cells in the Synthesis phase have increased.
Figure
3: Nuclear Grade set to III (high)
Figure
4: Nuclear Grade set to I (low)
Compare
the two models in Figure 3 and 4 above with the base level in
Figure 2. While the Nuclear Grade shares conditional dependence
with the same nodes, the behavior of those markers changes based
upon the aggressiveness of the tumor. The coloring in the graphical
model shows the change in population profile quickly and effectively.
It
is also possible to select two or more variables simultaneously.
The extent to which the HER2 oncoprotein expression and Estrogen
Expression affect tumor aggressiveness can be studied together.
If we wish to test hypotheses, we can modify any node and see
how our hypothesis affects the model. Notice how information flows
through the network.
Suppose
we are interested in examining how HER2 and Estrogen expression
levels affect Nuclear Grade as a prognostic indicator. We first
select these nodes and click "Graph" to display the states within
these nodes. This can be done for as many variables as we may
choose.
When
we change the levels of HER2 and Estrogen expression, we see very
clearly the prognostic indication for tumor severity and that
Estrogen receptor expression is a much stronger prognostic indicator
than HER2 expression. We can see this graphically in Figure 5.
Figure
5: Negative Estrogen receptor expression combined with positive
HER2 expression and correlation with Nuclear Grade
The
Reporter module can be used to create a report that will show
the conditional probabilities (or predicted likelihood) of any
target variables, given the expression of any independent variable(s).
Any part of the model, visualization can be pasted into Reporter
and then transferred into other applications. Figure 6 shows a
sample report.
Figure
6: A sample report listing the relationship between the prognostic
marker Nuclear Grade with the predictive and prognostic markers
of Estrogen receptor and HER2 expression levels
DecisionQ
sells predictive modeling software and complementary professional
services. Alternatively, components from FasterAnalytics
can be integrated into third party applications as part of broad
data management and analysis platform. If you have any further
questions or would like to schedule a more detailed demonstration
in person or over the web, please contact us.
DecisionQ
Corporation
531 Howard Street, 3rd Floor
San Francisco, CA 94105
www.decisionq.com
Phone: 415-357-1713
Fax : 415-276-6356
Email: info@decisionq.com
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