Personalized medicine offered a promise to fields that
require a nuanced approach to care. Oncology, neurology, and endocrinology are
all areas of medicine where there are seemingly no one size fits all cures. Because of this, there is a great need to
better understand the role of big data in making smart decisions about medical
treatments.
There are many ways to collect and
harvest big data to better determine the course of care for individuals
suffering from cancer, neurological, or metabolic diseases. By collecting many
types of data, from demographics to DNA to treatment response, scientists have
the resources to evaluate the disease state and begin to match groups of people
with specific treatment approaches.
There are several ways to analyze big data. Traditional approaches
such as binary logistic regression and discriminant function analysis aim to
classify people into treatment groups. Essentially, you can enter several
variables and these techniques create a model that decides which patient
belongs in which treatment group. Sensitivity (a person gets in the right
group) and specificity (the wrong person doesn’t get in the wrong group)
measurements allow the physician to understand how accurate the test model is,
and scientists can work to improve these models, and thus, treatment outcomes
(there are no perfect models).
One of the most exciting variables that can be added to
these models is data
from wearable technologies. Wearable electrodes, implants, and biosensors are
all capable of relaying data to prediction models in real time. This can
improve treatment outcomes by notifying physicians when treatments are working;
and when they’re not. By creating big data repositories, we are able to make
smart decisions about the future of treatment for the individual and for the
entire field.
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