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AI Biotech/Diagnostics: Cardio
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Application US20190362648
Published 2019-11-28
Accuracy Of Measuring Nutritional Responses In A Non-clinical Setting
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.
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- 1. A method, comprising:
receiving food data, wherein the food data indicates one or more foods consumed by an individual to evoke a nutritional response associated with a test performed in a non-clinical setting; receiving test data associated with performance of the test in the non-clinical setting; determining an accuracy of the test, based at least in part on two or more of the food data, the test data, second test data, or non-biomarker test data; and causing at least one of the following to be performed:
confirming the test data;
calculating the value of an associated biomarker;
capturing the value of two or more biomarkers from the same food data;
adjusting at least a portion of the test data;
repeating the test; or
adjusting a weighting of one or more of the food data or the test data utilized by a machine learning mechanism.
- 18. A system, comprising:
a data ingestion service, including one or more processors, configured to
receive test data associated with performance of a test by an individual in a non-clinical setting, wherein the test measures a nutritional response, and
a data accuracy service, including one or more processors, configured to
receive food data, wherein the food data indicates one or more foods consumed by the individual to evoke the nutritional response associated with the test performed in a non-clinical setting,
determine an accuracy of the test based at least in part on two or more of the food data, the test data, second test data, or non-biomarker test data; and
causing at least one of the following to be performed:
calculating the value of an associated biomarker;
capturing the value of two or more biomarkers from the same food data;
adjusting at least a portion of the test data;
repeating the test; or
adjusting a weighting of one or more of the food data or the test data utilized by a machine learning mechanism.
