Techniques for displaying results of computationally improved simulations

Abstract

A system and method for predictive disease identification via simulations improved using machine learning. A method includes applying a plurality of machine learning models including a plurality of first machine learning models and a second machine learning model to features extracted from data including animal characteristics data of at least one animal, wherein the second machine learning model is a combiner model, wherein outputs of the plurality of machine learning models include a plurality of disease predictor values, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types; running a plurality of disease contraction simulations based on the plurality of disease predictor values; and generating at least one display element based on results of the plurality of disease contraction simulations.

What is claimed is: 
     
         1 . A method for predictive disease identification via simulations improved using machine learning, comprising:
 applying a plurality of machine learning models including a plurality of first machine learning models and a second machine learning model to features extracted from data including animal characteristics data of at least one animal, wherein the second machine learning model is a combiner model, wherein outputs of the plurality of machine learning models include a plurality of disease predictor values, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types;   running a plurality of disease contraction simulations based on the plurality of disease predictor values; and   generating at least one display element based on results of the plurality of disease contraction simulations.   
     
     
         2 . The method of  claim 1 , wherein the at least one animal is a first animal, wherein generating the at least one display element further comprises:
 retrieving at least a portion of the results of the plurality of disease contraction simulations based on at least one input for at least one second animal, wherein the at least one display element is generated based on the retrieved at least a portion of the results of the plurality of disease contraction simulations.   
     
     
         3 . The method of  claim 2 , further comprising:
 storing the results of the plurality of disease contraction simulations, wherein the stored results are indexed based on a plurality of first combinations of factors including animal attributes, wherein the at least a portion of the results of the plurality of disease contraction simulations is retrieved based on a second combination of factors indicated in the at least one input for the at least one second animal.   
     
     
         4 . The method of  claim 1 , wherein the plurality of disease contraction simulations includes a plurality of temporal variation simulations for each of a plurality of respective time periods, wherein the at least one display element indicates at least a likelihood of contracting each of at least one predicted disease by a second animal in each of the plurality of time periods. 
     
     
         5 . The method of  claim 1 , wherein the plurality of disease predictor values is a plurality of second disease predictor values, wherein applying the plurality of machine learning models further comprises:
 applying the plurality of first machine learning models to the features extracted from the data including the animal characteristics data of the animal, wherein outputs of the plurality of first machine learning models includes a plurality of first disease predictor values, wherein each first disease predictor value corresponds to a respective disease type of the plurality of disease types; and   applying a combiner model to the plurality of first disease predictor values in order to output the plurality of second disease predictor values, wherein each second disease predictor value corresponds to one of the plurality of disease types, wherein the combiner model is a second machine learning model trained using a training data set including training outputs for the plurality of first machine learning models.   
     
     
         6 . The method of  claim 5 , wherein the plurality of first machine learning models includes a boosting ensemble of sequentially applied boosting machine learning models and at least one non-boosting machine learning model. 
     
     
         7 . The method of  claim 5 , wherein the plurality of first machine learning models includes a logistic regression model and at least one non-logistic regression model. 
     
     
         8 . The method of  claim 5 , wherein the wherein the plurality of first machine learning models includes a boosting ensemble and a logistic regression model. 
     
     
         9 . The method of  claim 5 , wherein the plurality of disease types includes at least one predetermined group of diseases. 
     
     
         10 . A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising:
 applying a plurality of machine learning models including a plurality of first machine learning models and a second machine learning model to features extracted from data including animal characteristics data of at least one animal, wherein the second machine learning model is a combiner model, wherein outputs of the plurality of machine learning models include a plurality of disease predictor values, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types;   running a plurality of disease contraction simulations based on the plurality of disease predictor values; and   generating at least one display element based on results of the plurality of disease contraction simulations.   
     
     
         11 . A system for predictive disease identification via simulations improved using machine learning, comprising:
 a processing circuitry; and   a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:   apply a plurality of machine learning models including a plurality of first machine learning models and a second machine learning model to features extracted from data including animal characteristics data of at least one animal, wherein the second machine learning model is a combiner model, wherein outputs of the plurality of machine learning models include a plurality of disease predictor values, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types;   run a plurality of disease contraction simulations based on the plurality of disease predictor values; and   generate at least one display element based on results of the plurality of disease contraction simulations.   
     
     
         12 . The system of  claim 11 , wherein the system is further configured to:
 retrieve at least a portion of the results of the plurality of disease contraction simulations based on at least one input for at least one second animal, wherein the at least one display element is generated based on the retrieved at least a portion of the results of the plurality of disease contraction simulations.   
     
     
         13 . The system of  claim 12 , wherein the system is further configured to:
 store the results of the plurality of disease contraction simulations, wherein the stored results are indexed based on a plurality of first combinations of factors including animal attributes, wherein the at least a portion of the results of the plurality of disease contraction simulations is retrieved based on a second combination of factors indicated in the at least one input for the at least one second animal.   
     
     
         14 . The system of  claim 11 , wherein the plurality of disease contraction simulations includes a plurality of temporal variation simulations for each of a plurality of respective time periods, wherein the at least one display element indicates at least a likelihood of contracting each of at least one predicted disease by a second animal in each of the plurality of time periods. 
     
     
         15 . The system of  claim 11 , wherein the plurality of disease predictor values is a plurality of second disease predictor values, wherein the system is further configured to:
 apply the plurality of first machine learning models to the features extracted from the data including the animal characteristics data of the animal, wherein outputs of the plurality of first machine learning models includes a plurality of first disease predictor values, wherein each first disease predictor value corresponds to a respective disease type of the plurality of disease types; and   apply a combiner model to the plurality of first disease predictor values in order to output the plurality of second disease predictor values, wherein each second disease predictor value corresponds to one of the plurality of disease types, wherein the combiner model is a second machine learning model trained using a training data set including training outputs for the plurality of first machine learning models.   
     
     
         16 . The system of  claim 15 , wherein the plurality of first machine learning models includes a boosting ensemble of sequentially applied boosting machine learning models and at least one non-boosting machine learning model. 
     
     
         17 . The system of  claim 15 , wherein the plurality of first machine learning models includes a logistic regression model and at least one non-logistic regression model. 
     
     
         18 . The system of  claim 15 , wherein the wherein the plurality of first machine learning models includes a boosting ensemble and a logistic regression model. 
     
     
         19 . The system of  claim 15 , wherein the plurality of disease types includes at least one predetermined group of diseases.