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US 12,744,108 B2Granted
Computer systems and methods for inferring scores for health metrics
AssigneeViome Life Sciences, Inc.
InventorsGuruduth S. Banavar, Helen Messier, Thomas Fabian, Alla Perlina, Harry Joel Tily, Matteo Rinaldi
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Abstract
Provided herein are computer systems and method for producing models that infer health scores for health metrics for a subject. A first learning algorithms is trained using raw feature data stored in computer memory to build a first model that infers feature cluster scores for each of a plurality of feature clusters in a feature group. A second learning algorithms is trained using the inferred feature cluster scores stored in computer memory to build a second model that infers a heath score for a health metric.
Claims24
A method that infers a health score for a health metric for a subject comprising:
- (a) providing a first data set comprising, for the subject, feature data for each of a plurality of features selected from one or more feature groups;
- (b) defining a plurality of feature clusters from the feature data, wherein each feature cluster in the plurality of feature clusters comprises a collection of features in the plurality of features;
- (c) executing a first computer model on the first data set to assign feature cluster scores for the health metric to each of the plurality of feature clusters, wherein the first computer model comprises a first machine learning algorithm, wherein each feature cluster score is a quantitative measure, thereby generating a second data set comprising a plurality of feature cluster scores;
- (d) executing a second computer model on the second data set comprising the plurality of feature cluster scores to infer a health score for the health metric for the subject, wherein the second computer model comprises a second machine learning algorithm;
- (e) obtaining, from the second computer model, a health score for the health metric for the subject that indicates a severity of the health metric that is present in the subject, wherein the severity is higher than a lowest category of severity;
- and
- (f) administering, responsive to determining the health score for the subject indicating the severity of the health metric is higher than the lowest category of severity, an intervention to the subject to improve the health score, thereby reducing the severity of the health metric in the subject, wherein the health metric is inflammation, and wherein the intervention comprises increased consumption of one or more of probiotics, fibers, and polyphenols.
4 more elements
A method comprising:
- a) training a first machine learning algorithm on a first training data set,
- wherein the first training data set comprises, for each of a plurality of objects, (1) feature data for each of a plurality of features and (2) a feature cluster label for each of one or a plurality of feature clusters, wherein the plurality of feature clusters is defined from the feature data and each feature cluster in the plurality of feature clusters comprises a collection of features in the plurality of features, and
- wherein the first machine learning algorithm develops a first model that infers a cluster score for each of the feature clusters based on the feature data, wherein the cluster score is a quantitative measure;
- b) executing the first model on a test data set comprising, for each of a plurality of objects, feature data for the features, to produce a cluster score data set comprising, for each of the plurality of objects in the test data set, a feature cluster scores for each of the feature clusters;
- c) labeling each subject in the cluster score data set with a label for a categorical variable to produce a second training data set;
- d) training a second machine learning algorithm on the second training data set to develop a second model that infers a label for the categorical variable based on feature cluster scores;
- e) using the first model and the second model to obtain a label for the categorical variable for a test subject, wherein the label for the categorical variable is an indication of a severity of inflammation present in the test subject, wherein the severity is higher than a lowest category of severity;
- and
- f) administering, responsive to the label for the categorical variable for the test subject indicating the severity of inflammation is higher than the lowest category of severity, an intervention to the test subject to improve the label for the categorical variable, thereby reducing the severity of inflammation, wherein the intervention comprises increased consumption of one or more of probiotics, fibers, and polyphenols.
6 more elements
Dependencies are inferred from the claim text.