According
to Mckinsey, data and machine learning in pharma can generate up to
$100 billion, annually. This will come from better decision making,
optimized innovations, and increased efficiency in clinical trials.
Many
big organizations have already begun integrating machine learning in
healthcare. These corporations are trying to identify ways to collect
and analyse all the data the healthcare industry has to improve
analysis of prevention methods and treatments. In fact, many pharmacy
colleges in Bangalore are also teaching students the
importance of combining machine learning and medicine.
There
are three major areas where machine learning is already making a
mark:
Diagnosis
Identifying
and diagnosing an ailment can be time consuming which is why it’s
at the forefront of machine learning research. Organizations are
focusing on integrating cognitive computing and genomic tumour
sequencing to prescribe the right medication. Google’s DeepMind
Health is working towards developing a technology that can address
macular degeneration in ageing eyes.
Personalized
Treatments
Personalized
medication is another area of focus for most organizations. They are
currently working on supervised learning. This allows doctors and
pharmacists to choose the right set of diagnosis based on symptoms
and genetic makeup of patients. IBM Watson Oncology is working on a
model that uses patients medical information and history to help
choose the best treatment options.
Clinical
Trial Research
Applying
machine learning to clinical trials research has multiple benefits.
Using predictive analytics can help choose the best candidate for a
clinical trial. For instance, examining the genetic information of an
area will result in picking the right candidate for a trial and make
the overall trial quick, and cost-effective.
There
are various other areas where machine learning is seeping in. If you
are planning to pursue a degree from one of the best
pharmacy colleges in Bangalore or another city, it is
essential to understand the importance of machine learning and big
data in the pharmaceutical industry.