How Machine Learning and Data Science Can Advance Nutrition Research
Jaden Stanford
2/24/2021
Author Information
insideBIGDATA
This article was written by the editorial team of insideBIGDATA. insideBIGDATA was founded on August 28th, 2011, and is a news outlet that distributes news, products and services, and strategies for data scientists and IT/business professionals. Their editorial focus is on big data, data science, AI, machine learning, and deep learning. The Editor-in-Chief of insideBIGDATA is Daniel Gutierrez, who is a professional data scientist working with AMULET Analytics in Los Angeles.
Article Summary
This article was published on February 24th, 2021. It discusses the opinions and research of Kyle Dardashti, CEO and Founder of Heali, a personalized nutrition company focused on supporting people with medical nutrition therapy. Heali has a highly diverse staff of experts in technology, nutrition, and medicine.
Although nutrition has been a popular subject of study for centuries, personalized nutrition is relatively new. With personalized nutrition, individuals can have specific dietary guidelines and plans based on factors like genetics, metabolism, disease, and environment. Machine learning and data science together could make personalized nutrition easily accessible for the population. Continuous tracking of data like meal habits, symptom patterns, physical activity, and lab test results could be combined and analyzed to provide personalized nutrition suggestions. These could include things like what to eat, when to eat, which foods to avoid, and dietary patterns to reduce undesireable symptoms.
A platform like this would also help advance nutrition research as a whole, because current research often fails to consider the numerous factors that can affect individual dietary needs. Machine learning and data science as applied to personalized nutrition could allow researchers to amass large quantities of data and identify complex patterns between age, disease, lifestyle, and diet.
This technology could also be used to develop in silico clinical trials, where statistical modelling would be used to create a synthetic patient pool based on previously-collected data. This could allow researchers to evaluate the effectiveness of potential diet plans quickly and without participant burden.
In-Silico Research Models
Overall, personalized nutrition utilizing machine learning and data science seems to be a promising approach to having more integrated healthcare in the future.
What do I think?
I think machine learning and data science applied to nutrition could offer amazing benefits and potentially change change healthcare for the better. I think when people normally go to look up diet plans or nutrition, they see generalized information or general recommendations for food groups. The reality is that everyone has different nutritional needs, and should therefore have personalized nutritional plans. It would be hard to individually make a plan for everyone that was interested, but if this was an automated process involving technology, it could be very effective and useful. I peronsally would love to have access to something like this.