SYSTEM AND METHOD FOR STRATIFYING AND MANAGING HEALTH STATUS
Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising receiving a set of biomarker values associated with a set of individuals; applying a machine learning model to the set of biomarker values to cluster the set of individuals based on the set of biomarker values; segmenting the set of individuals into a selected number of clusters based on the machine learning model; and determining a respective medical classification for each cluster of the selected number of clusters.
1 . A computer-implemented method comprising:
receiving, at a computing system, a set of biomarker values associated with a set of individuals;
applying, by the computing system, a machine learning model to the set of biomarker values to cluster the set of individuals based on the set of biomarker values;
segmenting, by the computing system, the set of individuals into a selected number of clusters based on the machine learning model; and
determining, by the computing system, a respective medical classification for each cluster of the selected number of clusters.
2 . The computer-implemented method of claim 1 , wherein the machine learning model is an unsupervised machine learning model.
3 . The computer-implemented method of claim 1 , wherein the set of biomarker values are associated with biomarkers that are readily available.
4 . The computer-implemented method of claim 3 , wherein the biomarkers include at least one of age, BMI, blood pressure, LDL, HDL, or A1C.
5 . The computer-implemented method of claim 1 , wherein the selected number of clusters is based on medical knowledge to position a cut on a dendrogram associated with the set of individuals.
6 . The computer-implemented method of claim 1 , wherein the respective medical classification for each cluster of the selected number of clusters is associated with a level of medical risk for one or more health conditions for individuals associated with the cluster.
7 . The computer-implemented method of claim 6 , wherein the one or more health conditions are associated with cardiometabolic health conditions.
8 . The computer-implemented method of claim 6 , further comprising:
associating, by the computing system, a selected cluster of the selected number of clusters with a level of medical risk for a first health condition;
identifying, by the computing system, in the selected cluster a range of biomarker values associated with at least one biomarker that was not known to be indicative of the first health condition; and
determining, by the computing system, that the range of biomarker values associated with the at least one biomarker is indicative of the first health condition.
9 . The computer-implemented method of claim 6 , wherein a cluster of the selected number of clusters comprises a subcluster associated with a first level of medical risk for a first health condition that is different from a second level of medical risk for one or more health conditions associated with the cluster.
10 . The computer-implemented method of claim 1 , further comprising:
for each cluster of the selected number of clusters, causing a determination of at least one respective action to be performed for individuals associated with the cluster, the at least one respective action including a medical screening or a medical intervention.
11 . A system comprising:
at least one processor; and
a memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
receiving a set of biomarker values associated with a set of individuals;
applying a machine learning model to the set of biomarker values to cluster the set of individuals based on the set of biomarker values;
segmenting the set of individuals into a selected number of clusters based on the machine learning model; and
determining a respective medical classification for each cluster of the selected number of clusters.
12 . The system of claim 11 , wherein the machine learning model is an unsupervised machine learning model.
13 . The system of claim 11 , wherein the set of biomarker values are associated with biomarkers that are readily available.
14 . The system of claim 13 , wherein the biomarkers include at least one of age, BMI, blood pressure, LDL, HDL, or A1C.
15 . The system of claim 11 , wherein the selected number of clusters is based on medical knowledge to position a cut on a dendrogram associated with the set of individuals.
16 . A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations comprising:
receiving a set of biomarker values associated with a set of individuals;
applying a machine learning model to the set of biomarker values to cluster the set of individuals based on the set of biomarker values;
segmenting the set of individuals into a selected number of clusters based on the machine learning model; and
determining a respective medical classification for each cluster of the selected number of clusters.
17 . The non-transitory computer-readable storage medium of claim 16 , wherein the machine learning model is an unsupervised machine learning model.
18 . The non-transitory computer-readable storage medium of claim 16 , wherein the set of biomarker values are associated with biomarkers that are readily available.
19 . The non-transitory computer-readable storage medium of claim 18 , wherein the biomarkers include at least one of age, BMI, blood pressure, LDL, HDL, or A1C.
20 . The non-transitory computer-readable storage medium of claim 16 , wherein the selected number of clusters is based on medical knowledge to position a cut on a dendrogram associated with the set of individuals.