The topic of machine learning in healthcare is a very well covered one, especially here on insideBIGDATA, with a treasure trove of opinions, news, and interviews.
However, what if we add to this and take a more holistic approach to health, describing it as more than just the “absence of illness?”
Wellness is a somewhat elusive concept, defined by the WHO as “a state of complete physical, mental, and social well-being, and not merely the absence of disease or infirmity.” Can machine learning contribute to our wellness?
It Does Start with the Absence of Illness
Health does feature prominently in the overall wellness of a person for the simple reason that being free of illness is the main prerequisite. In other words, suffering from a disease of some kind will trump everything else you do for your wellness.
There is no doubt that machine learning has established its role in healthcare with its capabilities. From the vaunted use of machine learning in the oncological field, through its use in dealing with rare diseases, to improving Alzheimer’s diagnosis, the current and future uses of machine learning in treating illnesses are numerous.
Another important health-related use for machine learning, which also has to do with wellness, is the prevention of acute, serious episodes and exacerbation of existing conditions. A great example of this is AireHealth with their platform and device for treating chronic lung disease (unfortunately very much in vogue due to Covid-19). Their platform analyzes real-time data from the device (a nebulizer) and lung sound analysis from the connected spirometer, recommending changes in treatment or other action meant to prevent acute episodes that often lead to emergency room visits.
Predicting the Mood
When we speak of mood in terms of wellness, we need to make a distinction between the mood in the medical sense (depression, anxiety, and other mood disorders) and the colloquial mood (happy, grumpy, etc.). Both these “types” of mood play an enormous role in the overall wellness of an individual, and there have been some very interesting uses of machine learning in helping people with both.
A great example of ML application to mood disorders is a 2019 study from South Korea, in which a team used machine learning to very successfully predict different types of episodes in patients with mood disorders (especially those suffering from bipolar disorders, where accuracy was near or above 90%).
A Chinese study from 2019 utilized machine learning to speed up the diagnostic process for patients with bipolar disorder. The technology helped prevent misdiagnosis as major depressive disorder and find the right treatment more quickly.
A number of attempts have also been made to apply machine learning to evaluate and even predict mood in the more colloquial sense of the word. For example, a team from the University Politehnica of Bucharest built a Mood Detector that included various sensors and a machine learning module to detect the mood of a person. They also added a music library to the app, meant to match the mood with the right soundtrack. A team from MIT used machine learning to predict the participants’ mood, stress levels, and physical health for tomorrow, using today’s data.
While physical and mental health play the lead roles in a person’s wellness, there are additional contributing factors.
Healthy sleep is one of them, becoming more and more elusive in the age of technology. The good news is that machine learning has shown great promise in promoting healthy sleeping habits, with a number of startups focusing on this application of ML.
Tempo and many other startups are applying machine learning to fitness, an inseparable part of staying healthy and happy. In most cases, the algorithms are used to analyze data from various devices and other data points to recommend highly-personalized and most effective fitness regimens.
Nutrition is another vital factor in wellness, and there have been a number of studies done into how machine learning models can be used to analyze a person’s eating habits and recommend healthy changes.
The factors which contribute to a person’s wellness are too numerous to cover in an article of this size, especially when we start including socio-cultural ones. Still, it’s quite obvious that machine learning has already made strides in this field. We’ll witness it become increasingly included in future wellness efforts, both personal and corporate.
About the Author
Natasha Lane is a lady of a keyboard with a rich history of working in the IT and digital marketing fields. She is always happy to collaborate with awesome blogs and share her knowledge all around the web. Besides content creating, Natasha is nowadays quite passionate about helping small business to grow strong.
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