PERSONALIZING FOOD RECOMMENDATIONS TO REDUCE GLYCEMIC RESPONSE

Provided herein are methods of generating models for predicting the glycemic response of an individual to consumption of a food or meal. The method involves collecting from a plurality of subjects, phenotypic data, ohmic data, and glycemic response data to foods or meals having a defined macronutrie...

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Bibliographische Detailangaben
Hauptverfasser:	PATRIDGE, Eric, PERLINA, Alla, BANAVAR, Guruduth S, GENKIN, Matvey, BASSEDA, Reza, VUYISICH, Momchilo, LE, Tiep Ba, GOPU, Vishakh, HEALD, Debra, GLINE, Stephanie, MESSIER, Helen, TILY, Hal
Format:	Patent
Sprache:	eng
Schlagworte:	ADVERTISING APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND,DEAF OR MUTE CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING CRYPTOGRAPHY DIAGRAMS DISPLAY EDUCATION EDUCATIONAL OR DEMONSTRATION APPLIANCES GLOBES HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS PHYSICS PLANETARIA SEALS
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Beschreibung
Zusammenfassung:	Provided herein are methods of generating models for predicting the glycemic response of an individual to consumption of a food or meal. The method involves collecting from a plurality of subjects, phenotypic data, ohmic data, and glycemic response data to foods or meals having a defined macronutrient profile. This data is then used to train a machine learning algorithm to create a model that predicts glycemic response of an individual to a food or meal based on the macro nutrient profile of the food or meal.