Prevention is better than cure. CURES to predict health problems with machine learning
Nowadays, regardless of a large amount of healthcare data produced during current practice, significant information that could deliberately support insights about a potential disease before it even starts causing problems remains unsettled or inaccessible to researchers.
Moreover, the current Electronic health records (EHRs) are ungainly, bulky and tend to slow the whole process down. Also, each patient’s medical data is regularly kept and controlled by each one’s local doctor (GP) which means this data is not immediately accessible to providers outside of this particular medical facility, nor to those ones outside the country.
Lastly, the medical schools around the globe are providing the students with an outdated, restricted and one-sided theoretical base that is just enabling them to “treat the sick one”.
Reading all this, you perhaps realize the analytical environment in healthcare meets a slew of challenges that urge a revolution in the sector.
This skill to predict changes in the human’s health could be especially valuable as our population is aging with a steady pace — according to the UN, people over 60 will be 50% of the global population by 2050.
It is now widely believed that the adequate employment of machine learning is one of our key weapons for fighting dangerous diseases and improving health. Do you support this belief?
To get you more versed in this topic, we’ve collected 3 ways how machine learning could alert patients and doctors about likely changes in their health months or years before the first symptoms even appear.
Analysing the way you speak and the words you use
To start, while X-rays and images can give signs about our physical health, our mental health often continues to be slightly tougher to diagnose. Unfortunately, mental diseases are now affecting more people than ever (more than 1/4 of the population on the globe).
Great news, everyonе! Machine learning is allowing brand new ways of identifying mental health conditions early by attuning into tell-tale signs sneaked in a human’s tone of voice, choice of words, voice and other refinements of language.
Nicole Martinez-Martin, a Ph.D., JD, a postdoctoral associate from the Stanford School of Biomedical Ethics has recently shared in one of her articles that:
“This way of using machine learning is foreseeable to bring major gains to psychiatric outcomes by improving prediction, diagnosis, and treatment of mental illness”.
Your eyes — the windows to your soul… and health
Heart disease is held for the highest percentage of death universally for both genders of all races.
Recently, few researchers at Google have proved that, by searching for trims in the crisscross of blood vessels, machine learning can be applied to predict an eventual chance of heart attack by looking into the human’s eyes.
What is happening inside your brain?
Unluckily, not all physical diseases are visible from the outside. For a very long period of time, practitioners worldwide have relied on X-rays and the scanning method to help patients diagnose the reason for particular symptoms. But how great it would be if they can detect these symptoms before they start to cause troubles?
It is now possible by using machine learning. An algorithm was already able to identify Alzheimer in a range of 6 years before doctors finally diagnosed patients with this treacherous condition.
Ben Franc, a savant of clinical radiology at Stanford University, states:
“Machine learning gives us the opportunity to harness the expertise of exposure to millions of cases. This can lead to early diagnosis and hopefully, more timely and effective treatment for patients.”
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CURES Token, the futuristic blockchain-based healthcare platform, is soon launching its international Electronic Health Records Platform. Stay tuned for more exciting news.
