computer a doctor’s ability to recognize
some types of heart disease by the characteristic shape of waves produced by
an electrocardiogram. She developed
a new function, called a constrained
non-rigid translation transform, which
could identify the similarity between
shapes in different ECG readouts.
the Power of Prediction
Although diagnosis and treatment are
key aspects of medical care, prediction
is also important, especially when it can
lead to early interventions. We all know,
for instance, that factors such as weight
and blood pressure can give an idea of
a person’s risk of heart disease. But
those sorts of risk scores are based on
population-wide models, says Shyam
Visweswaran, assistant professor of biomedical informatics at the University of
Pittsburgh. “If you build a model from
a group of people who are kind of similar to the current patient, you might do
better,” he says. Visweswaran has developed an algorithm that lets a computer
use clinical data to learn a model tailored to one specific patient and predict
outcomes for that person.
The computer takes all the data it has
on the patient, such as age, blood pressure, and lab results, and then picks one
variable and builds a model of all the
patients in its database who share that
variable. It could, for instance, compare everyone in the 50–55 age group.
“with a large number
available in electronic
physicians can now
benefit from the
opinion of their peers
on cases similar to
It builds a model for each variable it
can find, looks at which ones best fit
the patient at hand, and then averages
the best models to make a personalized
prediction of that patient’s outcome.
Whereas a population model only uses
a handful of variables considered to be
the best—it could be a simple checklist
of several risk factors, for instance—
this approach can potentially use any of
hundreds of variables. One additional
advantage is the machine might identify some factor that is predictive, but
that medical science was not previously
aware of, opening up new areas for research, Visweswaran says.
As with Suzuki’s pixel-based processing, this is another machine learning
method that has benefited from the
growth of processing power. As recently
as five years ago it might have taken a
half-hour to build all these models. Today it takes less than a minute, so the
machine can guide diagnoses in real
time during patient visits.
This approach could help predict an
intensive-care patient’s risk of an infection spreading to other organs, which
is a notoriously difficult task, and lead
to earlier or more aggressive treatment. It might help doctors decide, for
instance, which pneumonia patients
need to be admitted to the hospital
and which patients could be sent home
In the informatics program at Children’s Hospital Boston, assistant professor Ben Reis and his colleagues are
working on predicting a patient’s future
diagnoses years in advance. They have
developed Bayesian models that they
call Intelligent Histories, which comb
through the standard diagnostic codes
used for billing, to find patterns in a patient’s history that predict risk. In their
first application of the work, they discovered they could identify patients at
risk of domestic abuse as much as two
years before the doctors seeing those
patients first discovered the problem.
Doctors are supposed to screen patients for domestic abuse, but often
miss it until the problem becomes
acute, says Reis. Not only can the computer aid in screening for known signs
of abuse, it also picked up other diagnostic codes in the test that had not
been thought of as predictive, such as
infections, which might teach doctors
something about domestic abuse.
The Children’s Hospital Boston team
is working to expand their modeling to
other types of diagnoses. At the same
time, they are refining the machine
learning itself. For instance, “we’re trying to quantify how the quality of the
data that goes into the model affects the
results that come out,” says Reis.
As the world moves to a greater use
of electronic medical records, machine
learning is likely to play an even larger
role in clinical medicine, researchers
predict. Visweswaran says genetic data,
in particular, is going to require complicated computational models if it is going to be of value. Soon, experts expect,
the cost of gene sequencing will drop
to the point that individual genomes
will become part of people’s medical records, and will be available to the same
data mining and pattern recognition
approaches being applied to other data.
Genetic data will be too complicated
and too voluminous to be handled with
old-fashioned charting systems. “You
have to have computational tools to query this data,” Visweswaran says. “There’s
no way it can be done on paper.”
Gruhl, D., et al.
Aalim: A cardiac clinical decision support
system powered by advanced multi-modal
analytics, International Medical Informatics
Conference, Cape Town, South Africa, Sept.
Reis, B. Y., Kohane, I.S., and Mandl, K.D.
Longitudinal histories as predictors of
future diagnoses of domestic abuse:
modeling study, British Medical Journal,
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Syeda-Mahood, T., Beymer, D., and Wang, F.
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IEEE Engineering in Medicine and Biology
Society, August 23–27, 2007, Lyon, France.
Suzuki K., Zhang J., and Xu, J.
Massive-training artificial neural network
coupled with Laplacian-eigen function-based dimensionality reduction for
computer-aided detection of polyps in
CT colonography, IEEE Transactions on
Medical Imaging 29, 11, nov. 2010.
Visweswaran, S., Angus, D.C., Hsieh, M.,
Weissfeld, L., Yealy, D., and Cooper, G.F.
Learning patient-specific predictive models
from clinical data, Journal of Biomedical
Informatics 43, 5, Oct. 2010.
neil Savage is a science and technology writer based in