Disease Studies

Delivering personalized dietary advice for health management and disease prevention

Background and Objectives: Diet plays a huge role in health, both by increasing metabolic disease risks and acutely through adverse interactions with diseases and medications. Multimorbid and polypharmaceutical patients are at a particularly high risk of such interactions due to the number of drugs they take. This leads to avoidable hospitalizations and poor compliance. This study built and demonstrated a tool that provides personalized dietary advice that accounts for a patient’s combination of disease and drugs in real‑time on their mobile device.

Methods: A comprehensive list of validated drug‑disease‑food interactions from several reputable sources was constructed. This was compiled into a knowledge graph using the RACE array logic platform. This interactions knowledge graph was used to power a personalized dietary advisor application on a mobile device.

Results: Data from over 500,000 drug‑disease‑food interactions including 1,699 food ingredients and 9,526 disease interactions were compiled into a highly compressed knowledge model. This was used to inform recommendations for individual complex patients. It was also tested on virtual population of 10,000 multimorbid and polypharmaceutical patients.

Conclusions: This study showed that digital health tools can provide highly contextual and adaptive responses from a single knowledge graph. The study showed it is possible to provide highly personalized health advice to complex patients in real‑time on their own mobile device without having to hold such private information on a server. This enables highly secure, private and personalized digital health tools to be built.

To view the publication in full, please see https://www.digitmedicine.com/article.asp?issn=2226-8561;year=2018;volume=4;issue=3;spage=127;epage=132;aulast=Gardner#

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