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Why is Machine Learning Import to Clinical Medicine? May 10, 2017

Posted by Dawn Turzinski in Machine Learning.
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What is wrong with me? Why am I sick?  We have all been there, sitting in our chair trying to figure out what is going on with us.  We search the internet looking for answers.  But, we end up even more confused than we originally were.  Or maybe thinking the worst-case scenario.  We go from there to ending up in the doctor’s office listing all our symptoms.  As he/she pencils them down a diagnosis may be formed. What if we could improve our health care professional’s ability to establish a diagnosis.  Improvements in this area will be done by machine learning.

Anywhere you turn you turn, you see Machine Learning.  It is on Internet Searches learning your searches pulling in suggestions and posting them in Facebook.  It’s in the Google Car, learning how to drive itself.  It is on Amazon, learning your choices and showing what others bought the article you purchased and recommending something else. Machine learning is giving computers the capability to learn through learning algorithms without being programmed to do so.   So, how will we get there with Clinical medicine?

With advances in computational power, trending big data and Internet of things will bring change to many fields such as Clinical medicine.   For example, what if there were sensors attached to your body like the Fitbit that calculates data about your body and stores it.  Then a medical system would request this information from the sensor and pull that data to analyze it.  It would run a machine learning tool to figure out what is going on with you and give you a diagnosis.   Another example; you could pull data about a patient from other health/claim databases to allow the learning predictors to have access to lots of variables to lead to a prediction.  If we had this data available we might be able to predict cancer sooner rather than later.  With this information, we could improve patient diagnosis and help plan long term care for someone who could be terminal. What is another reason why machine learning is important to Clinical medicine?

Have you gone to the doctor and he/she orders you down to Radiology for some X-rays, MRI’s, CT Scans or etc.  What if these images were scanned in and a learning algorithm read them?  With advances in computer vision being applied to the big data will result in faster performance and be able to see things that a human eye may not see.  For example, if the machine knows precisely the anomaly that causes a certain seizure they can prepare the treatment plan for the patient.  And they know this by learning many scans with the same reading and know what the diagnosis is.   There will be no room for error with computers.

Now, we all have been frustrated with doctors; when they don’t know, or miss-diagnosis you.  Or have you go for testing that was not needed.  These errors are shocking to the medicine field and they would like to reduce them.  Which would reduce the risk and liability of the provider.  Machine learning algorithms would be able to generate possible diagnosis and suggest the tests that would be needed and reduce the over ordering of useless testing being done.  So, you might be asking when this will happen in the future?

This will happen slowly over the next couple of decades for many reasons.  Things will start to change with baby steps and not all at once.  Change is always hard for people who have been doing these repetitive tasks over the years.  For example, the people who read the x-rays will need to be re-trained/moved over to a new professional position.  Since their position will be replaced with a machine.  In addition to this the provider; will need to grow and understand the complex machine learning tools to succeed as a provider and understand their patient’s needs.  Advances will continue to happen slowly and machine learning is here to stay.

References:

  1. http://catalyst.nejm.org/big-data-machine-learning-clinical-medicine/
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