Identifying group and individual-level risk factors via risk-driven patient stratification

Abstract

Systems and methods for individual risk factor identification include identifying common risk factors for one or more risk targets from population data. Individuals are stratified into clusters based upon the common risk factors. A discriminability of each of the common risk factors is determined, using a processor, for a target cluster using individual data of the target cluster to provide re-ranked common risk factors as individual risk factors for the target cluster, such that the discriminability is a measure of how a risk factor discriminates its cluster from other clusters.

What is claimed is: 
     
         1 . A system for individual risk factor identification, comprising:
 a selection module configured to identify common risk factors for one or more risk targets from population data;   a clustering module configured to stratify individuals into clusters based upon the common risk factors; and   a ranking module configured to determine, using a processor, a discriminability of each of the common risk factors for a target cluster using individual data of the target cluster to provide re-ranked common risk factors as individual risk factors for the target cluster, such that the discriminability is a measure of how a risk factor discriminates its cluster from other clusters.   
     
     
         2 . The system as recited in  claim 1 , further comprising a group identification module configured to identify the clusters as one of a plurality of risk levels. 
     
     
         3 . The system as recited in  claim 2 , wherein the plurality of risk levels include at least one low-risk cluster and at least one high-risk cluster. 
     
     
         4 . The system as recited in  claim 2 , wherein the group identification module is further configured to identify the clusters as one of a plurality of risk levels based upon a proportion of at-risk individuals in each cluster. 
     
     
         5 . The system as recited in  claim 4 , wherein the group identification module is configured to identify at-risk individuals based upon a risk score. 
     
     
         6 . The system as recited in  claim 5 , wherein the group identification module is configured to assign the risk score using a classifier. 
     
     
         7 . The system as recited in  claim 3 , wherein the ranking module is further configured to compare risk factors for the target cluster with risk factors for at least one of: other high-risk clusters, low-risk clusters, and a general population. 
     
     
         8 . The system as recited in  claim 1 , wherein the ranking module is further configured to determine the discriminability by determining contributions of each risk factor in training a classifier. 
     
     
         9 . The system as recited in  claim 1 , wherein the ranking module is further configured to determine the discriminability by determining a difference in a frequency count-based distribution between each risk factor in the target cluster and the other clusters. 
     
     
         10 . The system as recited in  claim 1 , further comprising a validation module configured to validate each of the individual risk factors using the individual data by filtering out the common risk factors that do not indicate actual risk. 
     
     
         11 . The system as recited in  claim 1 , wherein the individual data includes one or more of: diagnosis, lab results, medication, hospitalization records, questionnaire data and genetic information for the target cluster. 
     
     
         12 . A system for individual risk factor identification, comprising:
 a selection module configured to identify common risk factors for one or more risk targets from population data;   a clustering module configured to stratify individuals into clusters based upon the common risk factors;   a group identification module configured to identify the clusters as one of a plurality of risk levels including at least one high-risk cluster and at least one low-risk cluster; and   a ranking module configured to determine, using a processor, a discriminability of each of the common risk factors for a target cluster using individual data of the target cluster to provide re-ranked common risk factors as individual risk factors for the target cluster, such that the discriminability is a measure of how a risk factor discriminates its cluster from other clusters, the other clusters including at least one of other high-risk clusters, low-risk clusters, and a general population.   
     
     
         13 . The system as recited in  claim 12 , wherein the group identification module is further configured to identify the clusters as one of a plurality of risk levels based upon a proportion of at-risk individuals in each cluster. 
     
     
         14 . The system as recited in  claim 13 , wherein the group identification module is further configured to identify at-risk individuals based upon a risk score. 
     
     
         15 . The system as recited in  claim 14 , wherein the group identification module is further configured to assigns the risk score using a classifier. 
     
     
         16 . The system as recited in  claim 12 , wherein the ranking module is further configured to determine the discriminability by determining contributions of each risk factor in training a classifier. 
     
     
         17 . The system as recited in  claim 12 , wherein the ranking module is further configured to determine the discriminability by determining a difference in a frequency count-based distribution between each risk factor in the target cluster and the other clusters. 
     
     
         18 . The system as recited in  claim 1 , further comprising a validation module configured to validate each of the individual risk factors using the individual data by filtering out the common risk factors that do not indicate actual risk. 
     
     
         19 . The system as recited in  claim 1 , wherein the individual data includes one or more of: diagnosis, lab results, medication, hospitalization records, questionnaire data and genetic information for the target cluster. 
     
     
         20 . A computer readable storage medium comprising a computer readable program for individual risk factor identification, wherein the computer readable program when executed on a computer causes the computer to perform the steps of:
 identifying common risk factors for one or more risk targets from population data;   stratifying individuals into clusters based upon the common risk factors; and   determining, using a processor, a discriminability of each of the common risk factors for a target cluster using individual data of the target cluster to provide re-ranked common risk factors as individual risk factors for the target cluster, such that the discriminability is a measure of how a risk factor discriminates its cluster from other clusters.