How Machine Learning and Data Science Can Advance Nutrition Research Posted on : Feb 24 - 2021

Nutrition plays a pivotal role in health and wellness. Diet-related diseases are the most common cause of death in the United States and diet alone is the leading risk factor for premature death worldwide.1 Fortunately, health conditions linked to poor diet are almost always preventable. However, individuals must understand the link between diet and disease in order to make appropriate lifestyle and behavior changes.

Even though food and nutrition has been studied for centuries, the field of nutrition has only recently accepted the idea of personalized nutrition: providing individuals with specific, actionable dietary insights based on genetics, metabolism, disease, and environment.

Advancing Personalized Nutrition Recommendations

Machine learning and data science bring exciting potential to the world of personalized nutrition. Combining these two technologies together on a cohesive platform that supports continuous tracking would allow for real-time validated nutrition recommendations tailored to an individual’s lifestyle. In other words, collecting data on an individual’s meal habits, symptom patterns, physical activity, and lab values can be compiled and analyzed to produce personalized suggestions on what to eat, when to eat, and why. For example, this platform could identify individual foods or ingredients that trigger specific symptoms and suggest a dietary pattern that is supported to reduce these symptoms. View More