
Three Ways on how Machine Learning and Data Science can Help in Nutrition Research
Although food and nutrition have been studied for centuries, the field of nutrition has only recently acknowledged the idea of personalized nutrition.
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CIO Applications Europe | Thursday, March 11, 2021

Although food and nutrition have been studied for centuries, the field of nutrition has only recently acknowledged the idea of personalized nutrition, that is, providing individuals with particular, actionable dietary insights according to genetics, metabolism, disease, and environment.
Fremont, CA: Nutrition plays an important part 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 globally. However, health conditions connected to poor diet are usually preventable. Individuals need to understand the connection between diet and disease to make the correct lifestyle and behavior changes.
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Here are three ways machine learning and data science can help in nutrition research:
Advancing Nutrition Research Efforts
Using machine learning and data science on an integrated platform would help identify complex links between age, disease, lifestyle, and diet on an individual and community level. Experts can gather this data to understand a person's nutrition needs further and confirm dietary recommendations.
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In-Silico Research Models
This technology can be used on a broader scale to create in silico clinical trials, which uses statistical modeling to create a synthetic patient pool with past user data. Since in silico trials are carried out within computer models, they can help explain cause and effect relationships quickly with zero participant burden. In silico trials, researchers can analyze the effectiveness of proposed diet therapies and understand how nutrition needs change across the lifecycle, even within vulnerable populations. These insights can be used to guide and enhance clinical care by offering medical professionals with authorized nutrition interventions tailored to the patient population.
Advancing Personalized Nutrition Recommendations
Integrating machine learning and data science on a consolidated platform that supports regular monitoring allows for real-time validated nutrition recommendations customized to a person's lifestyle. Meaning, gathering data on a person's meal habits, symptom patterns, physical activity, and lab values can be combined and studied to provide tailored suggestions on what, when, and why to eat.
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