Systems and Methods for Predicting Treatment-Regimen-Related Outcomes

Abstract

Systems and methods are provided for predicting treatment-regimen-related outcomes (e.g., risks of regimen-related toxicities). A predictive model is determined for predicting treatment-regimen-related outcomes and applied to a plurality of datasets. An ensemble algorithm is applied on result data generated from the application of the predictive model. Treatment-regimen-related outcomes are predicted using the predictive model. A combination of machine learning prediction and patient preference assessment is provided for enabling informed consent and precise treatment decisions.

It is claimed: 
     
         1 . A processor-implemented method for predicting regimen-related outcomes, the method comprising:
 accessing a predictive model that determines likelihoods of each of a plurality side effects for each of a plurality of treatment regimens based on one or more single-nucleotide polymorphisms (SNPs) associated with a patient;   predicting, using one or more data processors, regimen-related outcomes including side effects using the predictive model including likelihoods for each of the plurality of side effects;   providing a first interface for receiving indications of patient tolerances for side effects, wherein a numerical value is assigned to each of the plurality of side effects based on the received indications; and   providing a second interface that identifies the likelihoods for each of the plurality of side effects, wherein a treatment regimen for the patient is determined based on the likelihoods for each of the plurality of side effects and the numerical values assigned for each of the patient tolerances for side effects.   
     
     
         2 . The method of  claim 1 , further comprising:
 generating, using the one or more data processors, one or more training datasets and one or more testing datasets based at least in part on clinical data and gene feature data of a plurality of patients, the gene feature data including data related to one or more single-nucleotide polymorphisms (SNPs), and the clinical data;   determining, using one or more data processors, one or more initial predictive models using one or more machine learning algorithms based at least in part on the one or more training datasets;   applying, using the one or more data processors, the one or more initial predictive models on the one or more training datasets to generate result data;   performing, using the one or more data processors, an ensemble algorithm on the result data to generate ensemble data;   determining, using the one or more data processors, one or more final predictive models based at least in part on the ensemble data;   evaluating, using the one or more data processors, performance of the one or more final predictive models based at least in part on the one or more test datasets.   
     
     
         3 . The method of  claim 2 , wherein generating one or more training datasets and one or more testing datasets based at least in part on clinical data and gene feature data of a plurality of patients includes:
 determining a plurality of SNPs;   filtering the plurality of SNPs to determine one or more filtered SNPs;   determining the gene feature data based at least in part on the one or more filtered SNPs.   
     
     
         4 . The method of  claim 3 , wherein filtering the plurality of SNPs using a recursive partitioning operation for filtering by:
 dividing the gene feature dataset related to the plurality of SNPs into a plurality of sub-datasets;   selecting one or more first sub-datasets from the plurality of sub-datasets;   developing a first recursive partitioning model based at least in part on the one or more first sub-datasets;   determining one or more first predictive SNPs based at least in part on the first recursive partitioning model, wherein the one or more first predictive SNPs are included into the one or more filtered SNPs;   selecting one or more second sub-datasets from the plurality of sub-datasets;   developing a second recursive partitioning model based at least in part on the one or more second sub-datasets; and   determining one or more second predictive SNPs based at least in part on the second recursive partitioning model, wherein the one or more second predictive SNPs are included into the one or more filtered SNPs; and   
     
     
         5 . The method of  claim 3 , wherein generating one or more training datasets and one or more testing datasets based at least in part on clinical data or gene feature data of a plurality of patients includes:
 determining the gene feature data based at least in part on one or more predetermined SNPs.   
     
     
         6 . The method of  claim 3 , wherein filtering the plurality of SNPs to determine the one or more filtered SNPs includes:
 removing a number of SNPs based on missing data from the plurality of SNPs.   
     
     
         7 . The method of  claim 3 , wherein filtering the plurality of SNPs to determine the one or more filtered SNPs includes:
 removing one or more SNPs that are associated from the plurality of SNPs.   
     
     
         8 . The method of  claim 2 , wherein the one or more machine learning algorithms correspond to one or more of the following: a penalized logistic regression algorithm, a random forests algorithm, and a C5.0 algorithm. 
     
     
         9 . The method of  claim 2 , wherein generating one or more training datasets and one or more testing datasets based at least in part on clinical data or gene feature data of a plurality of patients includes:
 generating one or more clinical predictor datasets based at least in part on the clinical data; and   generating one or more gene feature datasets based at least in part on the gene feature data.   
     
     
         10 . The method of  claim 9 , wherein applying the one or more initial predictive models on the one or more training datasets to generate result data includes:
 applying the initial predictive models on the one or more clinical predictor datasets to generate clinical result data; and   applying the initial predictive models on the one or more gene feature datasets to generate gene feature result data.   
     
     
         11 . The method of  claim 2 , wherein the ensemble algorithm corresponds to an average calculation or a logistic regression algorithm. 
     
     
         12 . The method of  claim 2 , wherein generating one or more training datasets and one or more testing datasets based at least in part on clinical data or gene feature data of a plurality of patients includes:
 generating one or more clinical predictor datasets by generating binary predictor data based at least in part on the clinical data.   
     
     
         13 . The method of  claim 2 , further comprising:
 performing 10-fold cross-validation on the one or more training datasets to determine one or more tuning parameters for the initial predictive models.   
     
     
         14 . The method of  claim 1 , wherein the first interface displays a scale running from perfect health to death. 
     
     
         15 . The method of  claim 11 , wherein low magnitude numerical values are assigned to less tolerable side effects, where a magnitude of zero is associated with death. 
     
     
         16 . The method of  claim 11 , wherein the second interface further displays a scale running from perfect health to death that includes the plurality of side effects, the plurality of side effects being positioned on the scale according to the numerical values assigned to each of the plurality of side effects. 
     
     
         17 . A computer-implemented system for predicting regimen-related outcomes, comprising:
 one or more data processors;   one or more non-transitory computer-readable mediums encoded with instructions for commanding the one or more data processors to execute steps of a method that includes:
 accessing a predictive model that determines likelihoods of each of a plurality side effects for each of a plurality of treatment regimens based on one or more single-nucleotide polymorphisms (SNPs) associated with a patient; 
 predicting, using one or more data processors, regimen-related outcomes including side effects using the predictive model including likelihoods for each of the plurality of side effects; 
 providing a first interface for receiving indications of patient tolerances for side effects, wherein a numerical value is assigned to each of the plurality of side effects based on the received indications; and 
 providing a second interface that identifies the likelihoods for each of the plurality of side effects, wherein a treatment regimen for the patient is determined based on the likelihoods for each of the plurality of side effects and the numerical values assigned for each of the patient tolerances for side effects. 
   
     
     
         18 . The system of  claim 17 , wherein the method further comprises:
 generating, using the one or more data processors, one or more training datasets and one or more testing datasets based at least in part on clinical data and gene feature data of a plurality of patients, the gene feature data including data related to one or more single-nucleotide polymorphisms (SNPs), and the clinical data;   determining, using one or more data processors, one or more initial predictive models using one or more machine learning algorithms based at least in part on the one or more training datasets;   applying, using the one or more data processors, the one or more initial predictive models on the one or more training datasets to generate result data;   performing, using the one or more data processors, an ensemble algorithm on the result data to generate ensemble data;   determining, using the one or more data processors, one or more final predictive models based at least in part on the ensemble data;   evaluating, using the one or more data processors, performance of the one or more final predictive models based at least in part on the one or more test datasets.   
     
     
         19 . The system of  claim 18 , wherein generating one or more training datasets and one or more testing datasets based at least in part on clinical data and gene feature data of a plurality of patients includes:
 determining a plurality of SNPs;   filtering the plurality of SNPs to determine one or more filtered SNPs;   determining the gene feature data based at least in part on the one or more filtered SNPs.   
     
     
         20 . A non-transitory computer-readable medium encoded with instructions for commanding one or more data processors to execute steps of a method for predicting regimen-related outcomes, the method comprising:
 accessing a predictive model that determines likelihoods of each of a plurality side effects for each of a plurality of treatment regimens based on one or more single-nucleotide polymorphisms (SNPs) associated with a patient;   predicting, using one or more data processors, regimen-related outcomes including side effects using the predictive model including likelihoods for each of the plurality of side effects;   providing a first interface for receiving indications of patient tolerances for side effects, wherein a numerical value is assigned to each of the plurality of side effects based on the received indications; and   providing a second interface that identifies the likelihoods for each of the plurality of side effects, wherein a treatment regimen for the patient is determined based on the likelihoods for each of the plurality of side effects and the numerical values assigned for each of the patient tolerances for side effects.