Abstract: Techniques are disclosed herein for improving the accuracy of test data obtained outside of a clinical setting. Using the technologies described herein, different techniques can be utilized to analyze, score and adjust test data associated with one or more “at home” tests. In some examples, computing systems are utilized to generate quality scores indicating the accuracy of the test data associated with a particular biomarker. In other examples, an authorized user, such as a data manager can analyze the test data utilizing a user interface to generate scores and/or adjust the test data.

Abstract: Techniques are disclosed herein for improving the accuracy of nutritional responses measured in a non-clinical setting. Using the technologies described herein, different techniques can be utilized to improve the accuracy of test data associated with one or more “at home” tests. In some examples, more than one test is utilized to improve the accuracy of test data associated with a particular biomarker. In other examples, a data accuracy service can programmatically analyze data received from an individual and determine whether the data is accurate. In some examples, a computing device is utilized to assist in determining what food item(s) are consumed, as well as determine whether a test protocol was followed.

Abstract: Techniques are disclosed herein for using standardized meals and digital devices can be utilized outside a clinical setting to assist in determining/predicting one or more clinical states associated with one or more individuals. Using the technologies described herein, different techniques can be utilized to for using nutritional response measurements. For example, response measurements can be obtained for two fat meals instead of a single fat meal. Data associated with a circadian rhythm (e.g., sleep times, awake times) can also be utilized to improve the accuracy of the measured biomarkers. In other examples, biome data and other data can be utilized.

Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations using predicted values of target biomarkers. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of target biomarkers that are associated with an individual. Personalized nutritional recommendations are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, ketone data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted value of the target biomarkers. A nutrition service utilizes the predicted value of the target biomarkers when generating the personalized nutritional recommendations.

Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations using predicted values of target biomarkers. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of target biomarkers that are associated with an individual. Personalized nutritional recommendations are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, ketone data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted value of the target biomarkers. A nutrition service utilizes the predicted value of the target biomarkers when generating the personalized nutritional recommendations.

Abstract: Techniques are disclosed herein for improving the accuracy of test data obtained outside of a clinical setting. Using the technologies described herein, different techniques can be utilized to analyze, score and adjust test data associated with one or more “at home” tests. In some examples, computing systems are utilized to generate quality scores indicating the accuracy of the test data associated with a particular biomarker. In other examples, an authorized user, such as a data manager can analyze the test data utilizing a user interface to generate scores and/or adjust the test data.

Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations for foods available from one or more food sources. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of personalized nutrition data, such as one or more target biomarkers, that are associated with an individual after eating the foods. Personalized nutritional recommendations for foods available from the food sources are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, triglycerides data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted personalized nutrition data. A nutrition service utilizes the predicted personalized nutrition data when generating the personalized nutritional recommendations.

Abstract: Techniques are disclosed herein for using standardized meals and digital devices can be utilized outside a clinical setting to assist in determining/predicting one or more clinical states associated with one or more individuals. Using the technologies described herein, different techniques can be utilized to for using nutritional response measurements. For example, response measurements can be obtained for two fat meals instead of a single fat meal. Data associated with a circadian rhythm (e.g., sleep times, awake times) can also be utilized to improve the accuracy of the measured biomarkers. In other examples, biome data and other data can be utilized.

Abstract: Techniques are disclosed herein for improving the accuracy of nutritional responses measured in a non-clinical setting. Using the technologies described herein, different techniques can be utilized to improve the accuracy of test data associated with one or more “at home” tests. In some examples, more than one test is utilized to improve the accuracy of test data associated with a particular biomarker. In other examples, a data accuracy service can programmatically analyze data received from an individual and determine whether the data is accurate. In some examples, a computing device is utilized to assist in determining what food item(s) are consumed, as well as determine whether a test protocol was followed.

Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations using predicted values of target biomarkers. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of target biomarkers that are associated with an individual. Personalized nutritional recommendations are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, ketone data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted value of the target biomarkers. A nutrition service utilizes the predicted value of the target biomarkers when generating the personalized nutritional recommendations.

Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations using predicted values of target biomarkers. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of target biomarkers that are associated with an individual. Personalized nutritional recommendations are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, ketone data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted value of the target biomarkers. A nutrition service utilizes the predicted value of the target biomarkers when generating the personalized nutritional recommendations.