Systems and Methods for Predicting Medications to Prescribe to a Patient Based on Machine Learning

Abstract

A system for predicting medications to prescribe to a patient includes a text encoding module and a medication prediction module. The text encoding module is configured to obtain a clinical-information vector from clinical information of the patient. The medication prediction module configured to apply a machine-learned medication-prediction algorithm to the clinical-information vector to select a subset of medications to prescribe to the patient. The machine-learned medication-prediction algorithm is designed with a diversity-promoting regularization model, and is configured to simultaneously consider correlations among different medications and dependencies between patient information and medications when selecting a subset of medications to prescribe to the patient.

1 . A method of predicting medications to prescribe to a patient, the method comprising:
 obtaining a clinical-information vector from clinical information of the patient; and   applying a machine-learned medication-prediction algorithm to the clinical-information vector to select a subset of medications to prescribe to the patient.   
     
     
         2 . The method of  claim 1 , wherein applying a medication-prediction algorithm to the clinical-information vector comprises, for each medication in a set of medications:
 determining a score function representing a dependency between the patient's clinical information and the medication; and   including the medication in the subset of medications if the score function satisfies a criterion.   
     
     
         3 . The method of  claim 2 , wherein the medications included in the subset of medications to prescribe to the patient are selected from a first set of medications i and a second set of medications j, and determining a score function comprises obtaining a measure of correlation between a medication in the first set of medication and a medication in the second set of medications. 
     
     
         4 . The method of  claim 3 , wherein determining a score function comprises implementing a determinantal point process to obtain the measure of correlation. 
     
     
         5 . The method of  claim 4 , wherein determining a score function further comprises implementing a deep conditional determinantal point process to obtain a measure of dependency between a clinical condition and a pair of correlated medications. 
     
     
         6 . The method of  claim 4 , wherein determining a score function further comprises implementing a relation-regularized deep conditional determinantal point process to obtain a measure of dependency between a clinical condition and a pair of correlated medications, wherein the correlation among medications accounts for synergistic and antagonistic interactions. 
     
     
         7 . The method of  claim 4 , wherein determining a score function further comprises implementing a diversity-promoting regularization model. 
     
     
         8 . The method of  claim 3 , wherein obtaining a measure of correlation comprises calculating a distance metric using a projection matrix, where the row vectors of the projection matrix project representation vectors of a first set of medications i and a second set of medications j into a lower-dimensional latent space. 
     
     
         9 . The method of  claim 3 , wherein obtaining a measure of correlation comprises calculating uncorrelation between representation vectors of a first set of medications i and a second set of medications j. 
     
     
         10 . The method of  claim 9 , wherein uncorrelation is calculated using eigenvalues of component matrices composed from representation vectors of a first set of medications i and a second set of medications j, wherein uniformity among the eigenvalues measures uncorrelation between components. 
     
     
         11 . The method of  claim 10 , wherein eigenvalues are promoted to be uniform in order to promote evenness between components. 
     
     
         12 . The method of  claim 10 , further including normalizing the eigenvalues into a probability simplex and encouraging the discrete distribution parameterized by the normalized eigenvalues to have small Kullback-Leibler (KL) divergence with the uniform distribution. 
     
     
         13 . The method of  claim 12 , further comprising calculating a distance metric based on similarity between the normalized eigenvalues between the representation vectors of a first set of medications i and a second set of medications j. 
     
     
         14 . The method of  claim 3 , wherein the vectors are encouraged to be near-orthogonal to promote diversity between the components. 
     
     
         15 . A system predicting medications to prescribe to a patient, the system comprising:
 a text-encoding module configured to obtain a clinical-information vector from clinical information of the patient; and   a medication prediction module configured to apply a machine-learned medication-prediction algorithm to the clinical-information vector to select a subset of medications to prescribe to the patient.