Computational Intelligence for Deriving Knowledge from Complex Data
Bruce Hunter and Jim Duckett
Managing Partners
Gary B. Fogel, Ph.D.
Chief Executive Officer
Computational Intelligence for Deriving Knowledge from Complex Data
The recent and growing proliferation of data offers a host of opportunities. For example
one might use data to determine how to:
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Assign dynamic levels of risk to loan portfolios based on their previous history
and current conditions.
Segment patients according to the risks for development of significant medical
conditions such as advanced heart disease, diabetes, and cancer.
Price and adapt commodities or services to optimize revenue
Suggest which movie a consumer might enjoy the most, or a neighborhood that
might suit them best in a new city.
And on . . .
While these opportunities are real, in most cases, taking advantage of them can be
extremely complicated. It is not always apparent which data is important to consider and
the human expert is easily overwhelmed by the volume and velocity of the data. It may
also be the case that combinations of multiple factors lead to true predictability, and the
combination is not easily understood even when using statistical approaches.
Relationships between variables can be complicated and nonlinear. In order to take
advantage of the volume and velocity of data, analytics are required that can 1) handle
a wide range of data and discover relevant features automatically, 2) identify complex
relationships between very large numbers of variables, and 3) adapt their behavior over
time in response to changes in the system being modeled.
Phoenix-based DHx Software and San Diego-based Natural Selection, Inc. (NSI) have
partnered to help our joint customers take advantage of the powerful insights within their
data. In addition to our ability to create custom application software, ranging from SAAS
and eCommerce applications to mobile apps, DHx has expertise in uniting diverse data
sources into a coherent database. We help our customers identify potential sources of
data--both internal and external--and we build the systems that bring that data into a
manageable format.
Over the last 20 years, NSI has developed proprietary computational intelligence
methods for problem solving and has applied these tools to problems in industry,
medicine, and defense. NSI tools afford the opportunity to discover combinations of
important features in data in dynamic fashion. For example, NSI has developed:
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A dynamic inspection algorithm called PREDICT for the U.S. Food and Drug
Administration (FDA) that is currently fielded nation-wide to assist in product
inspection at ports of entry.
Models to help optimize complex processes to produce outstanding results in an
efficient manner.
Methods of detecting threats to large, complicated networks.
Biomarkers that help clinicians determine if a patient is or is not appropriate for a
particular cancer treatment based on clinical, genetic and demographic
information.
The FDA uses the PREDICT tool to reduce the risk of potentially unsafe products
entering the commercial sector and expediting the entry rate for those products that are
in compliance with FDA regulations, thereby increasing the efficiency and efficacy of
FDA inspectors. The tool can be updated regularly in light of new data and NSI holds
two patents on the approach. NSI received an FDA Honor Award for its effort on the
project and the PREDICT platform is currently fielded nationwide at US ports of entry.
This same approach--allowing the system to identify significant combinations of the
features within the data and to make actionable recommendations--can be used across
a range of fields, including for example in identifying the best offer to make to a
consumer at which time in which venue, or which commodity--such as a movie--would
be best for which particular consumer.
NSI also has significant experience developing models that enable a broad range of
companies to optimize logistical operations and processes to improve efficiency. For
example working with the South East Alaska Pilots Association, NSI has recently helped
solve a exceedingly complicate scheduling problem of assigning ship-board pilots to
cruise ships in Alaskan waters. Given many problem constraints, the number of feasible
pilot schedules is too large to be searched using traditional methods. NSI has delivered
a scheduler that can be used to identify pilot assignment schedules that satisfy all of the
constraints and also minimize travel costs to the Association. Despite the number of
possible schedule solutions, the NSI scheduler can solve this in less than 1 hour.
NSI has also helped improve production at energy plants. For instance, given a
production facility, the operators in charge have the task of knowing which control
functions to change at specifically what times in the process to maximize production.
Many of these control features act together in nonlinear ways, making the problem of
knowing which of many “knobs” to turn in what order quite difficult. Using large sets of
data about the production process and operator functions it is possible to model this
system using nonlinear models and provide feedback to the operator that helps the
human-in-the-loop maximize efficiency. A byproduct of this function is that the operator
also learns useful control strategies over time.
Another example of the power of the NSI approach can be seen in the medical arena.
NSI CEO Dr. Gary Fogel has a Ph.D. in biology and has focused on these applications
for over 15 years. NSI has done work, for example, applying statistics and machine
learning for patient segmentation or to predict which patients would likely benefit from a
specific cancer immunotherapy. Considering demographic, genomic, proteomic, and
radiological features, these models help to identify the best therapeutic approach for
individual patients. The models can also anticipate when a treatment regimen is likely to
become less effective and to point to when and how treatment should be modified.
Similar models can be developed for other major diseases in order to improve patient
outcomes while reducing time and money invested in ineffective treatments. For
example, it is possible to develop a model that would segment heart patients into
groups based on who is likely to benefit from drug therapies and those who are likely to
need a surgical intervention.
While these examples span a range of industries, they all highlight the skills DHx and
NSI bring to bear to collect and manage complex data and to use evolutionary
computation to identify keys features within the data that are related to predicting or
optimizing a specific outcome and to identifying the complex relationships between
those features to create an effective model. Finally, once the model has been
developed, DHx can integrate those models into existing applications in order to make
sure the information is available at the right point in the decision-making process.
To learn more about our predictive analytics offering, please contact:
Bruce Hunter
[email protected]
+1 602 570 5324
or
Jim Duckett
[email protected]
+1 602 677 2815
DHx is a Phoenix-based software development firm with architecture, development, and
test resources in Arizona and off-shore. We provide our customers with custom
application software, ranging from SAAS and eCommerce applications to mobile apps.
We have significant expertise and experience in designing and developing complex
databases as well as migrating data between enterprise applications for Fortune 500
customers. We also have expertise in integrating disparate back-end systems. We have
partnered with Natural Selection to provide their unique analytics capabilities to our own
customers.
NSI is a recognized world-wide leader in the application of state-of-the-art
computational intelligence to complex optimization problems. Its principals are
pioneering experts in the field. They have published extensively in peer-reviewed
journals on the theory and application of these methods to complex problems. Dr. David
Fogel is President of Natural Selection, Inc. He is an award-winning scientist with over
200 publications, 6 books, and is the former president of the IEEE Computational
Intelligence Society. Dr. Gary Fogel is CEO of Natural Selection, Inc. He is an
internationally recognized leader in the application of computational intelligence for
large data sets, as found commonly in bioinformatics, homeland security, and
commercial process optimization. NSI’s CONNECT® tools can be used to design
automated systems for pattern recognition, where models learn on their own to identify
features or collections of features that are useful for making predictions. Once
formalized, this information can be used by human experts to better understand the
system, or understand new features such as biomarkers, etc. Our expertise has been
recognized not just by the US government, but also by the IEEE, the world’s largest
organization of professional engineers. Our experience across a wide variety of
problems allows us to see insights that others may overlook.