Techniques for disease prediction using machine learning-improved simulations and for generating display elements using simulation results
Techniques for predictive disease identification using simulations improved via machine learning. A method includes applying at least one machine learning model to features extracted from data including animal characteristics data of an animal, wherein outputs of the at least one machine learning model include a plurality of disease predictor values, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types, wherein each disease type of the plurality of disease types corresponds to a predetermined group of diseases; generating disease contraction statistics based on the outputs of the at least one machine learning model; and determining, based on the disease contraction statistics, at least one disease prediction for the animal.
1. A method for predictive disease identification, comprising:
applying a plurality of first machine learning models based on training data in order to generate a first set of outputs, wherein the plurality of first machine learning models includes an ensemble of boosting machine learning models and a logistic regression model, wherein the ensemble of boosting machine learning models is sequentially trained using a boosting algorithm in a sequence, wherein misclassifications by a model among the ensemble of boosting machine learning models in the sequence are used to adjust weights of subsequent models among the ensemble of boosting machine learning models in the sequence;
training a second machine learning model based on the first set of outputs, wherein the second machine learning model is a combiner model trained to output a plurality of disease predictor values based on the first set of outputs, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types, wherein each disease type of the plurality of disease types corresponds to a predetermined group of diseases;
applying the plurality of first machine learning models and the second machine learning model based on features extracted from data including animal characteristics data of an animal, wherein outputs of the plurality of first machine learning models and the second machine learning model include a plurality of disease predictor values, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types, wherein each disease type of the plurality of disease types corresponds to a predetermined group of diseases;
determining a plurality of simulation parameters based on the plurality of disease predictor values, wherein the plurality of simulation parameters define at least a plurality of time periods for which a plurality of disease contraction simulations are to be run;
running the plurality of disease contraction simulations based on the plurality of simulation parameters in order to obtain simulation results, wherein running the plurality of disease contraction simulations includes providing the plurality of disease predictor values to a simulation engine configured to determine predictions of diseases for animals, wherein the plurality of disease contraction simulations are run for the plurality of time periods defined in the plurality of simulation parameters;
generating disease contraction statistics based on the simulation results; and
determining, based on the disease contraction statistics, at least one disease prediction for the animal.
2. The method of claim 1 , further comprising:
generating at least one insight based on the at least one disease prediction for the animal, wherein the at least one insight includes a likelihood of developing each of at least one disease at each of a plurality of stages of development of the animal.
3. The method of claim 2 , wherein the plurality of stages of development of the animal includes at least one of puppy, young, mature, and geriatric.
4. The method of claim 1 , further comprising:
generating at least one insight based on the at least one disease prediction for the animal, wherein the at least one insight includes at least one expected cost of health treatment for the animal.
5. 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 first machine learning models based on training data in order to generate a first set of outputs, wherein the plurality of first machine learning models includes an ensemble of boosting machine learning models and a logistic regression model, wherein the ensemble of boosting machine learning models is sequentially trained using a boosting algorithm in a sequence, wherein misclassifications by a model among the ensemble of boosting machine learning models in the sequence are used to adjust weights of subsequent models among the ensemble of boosting machine learning models in the sequence;
train a second machine learning model based on the first set of outputs, wherein the second machine learning model is a combiner model trained to output a plurality of disease predictor values based on the first set of outputs, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types, wherein each disease type of the plurality of disease types corresponds to a predetermined group of diseases;
applying the plurality of first machine learning models and the second machine learning model based on features extracted from data including animal characteristics data of an animal, wherein outputs of the the plurality of first machine learning models and the second machine learning model include a plurality of disease predictor values, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types, wherein each disease type of the plurality of disease types corresponds to a predetermined group of diseases;
determine a plurality of simulation parameters based on the plurality of disease predictor values, wherein the plurality of simulation parameters define at least a plurality of time periods for which a plurality of disease contraction simulations are to be run;
run the plurality of disease contraction simulations based on the plurality of simulation parameters in order to obtain simulation results, wherein running the plurality of disease contraction simulations includes providing the plurality of disease predictor values to a simulation engine configured to determine predictions of diseases for animals, wherein the plurality of disease contraction simulations are run for the plurality of time periods defined in the plurality of simulation parameters;
generate disease contraction statistics based on the simulation results; and
determine, based on the disease contraction statistics, at least one disease prediction for the animal.
6. The system of claim 5 , wherein the system is further configured to: generate at least one insight based on the at least one disease prediction for the animal, wherein the at least one insight includes a likelihood of developing each of at least one disease at each of a plurality of stages of development of the animal.
7. The system of claim 6 , wherein the plurality of stages of development of the animal includes at least one of puppy, young, mature, and geriatric.
8. The system of claim 5 , wherein the system is further configured to:
generate at least one insight based on the at least one disease prediction for the animal, wherein the at least one insight includes at least one expected cost of health treatment for the animal.
9. A method for predictive disease identification, comprising:
applying a plurality of first machine learning models based on training data in order to generate a first set of outputs, wherein the plurality of first machine learning models includes an ensemble of boosting machine learning models and a logistic regression model, wherein the ensemble of boosting machine learning models is sequentially trained using a boosting algorithm in a sequence, wherein misclassifications by a model among the ensemble of boosting machine learning models in the sequence are used to adjust weights of subsequent models among the ensemble of boosting machine learning models in the sequence;
training a second machine learning model based on the first set of outputs, wherein the second machine learning model is a combiner model trained to output a plurality of disease predictor values based on the first set of outputs, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types, wherein each disease type of the plurality of disease types corresponds to a predetermined group of diseases;
applying the plurality of first machine learning models and the second machine learning model based on features extracted from data including animal characteristics data of at least one animal, wherein outputs of the plurality of first machine learning models and the second machine learning model include a plurality of disease predictor values, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types, wherein each disease type of the plurality of disease types corresponds to a predetermined group of diseases;
determining a plurality of simulation parameters based on the plurality of disease predictor values, wherein the plurality of simulation parameters define at least a plurality of time periods for which a plurality of disease contraction simulations are to be run;
running the plurality of disease contraction simulations based on the plurality of simulation parameters in order to obtain simulation results, wherein running the plurality of disease contraction simulations includes providing the plurality of disease predictor values to a simulation engine configured to determine predictions of diseases for animals, wherein the plurality of disease contraction simulations are run for the plurality of time periods defined in the plurality of simulation parameters; and
generating at least one display element based on the outputs of the the plurality of first machine learning models and the second machine learning model and the simulation results.
10. The method of claim 9 , further comprising:
determining at least one disease prediction for the animal based on the outputs of the the plurality of first machine learning models and the second machine learning model, wherein the at least one display element is generated based further on the at least one disease prediction.
11. The method of claim 10 , further comprising:
generating at least one insight based on the at least one disease prediction for the animal, wherein the at least one insight includes a likelihood of developing each of at least one disease at each of a plurality of stages of development of the animal.
12. The method of claim 11 , wherein the plurality of stages of development of the animal includes at least one of puppy, young, mature, and geriatric.
13. The method of claim 10 , further comprising:
generating at least one insight based on the at least one disease prediction for the animal, wherein the at least one insight includes at least one expected cost of health treatment for the animal.
14. A method for predictive disease identification via simulations improved using machine learning, comprising:
applying a plurality of first machine learning models based on training data in order to generate a first set of outputs, wherein the plurality of first machine learning models includes an ensemble of boosting machine learning models and a logistic regression model, wherein the ensemble of boosting machine learning models is sequentially trained using a boosting algorithm in a sequence, wherein misclassifications by a model among the ensemble of boosting machine learning models in the sequence are used to adjust weights of subsequent models among the ensemble of boosting machine learning models in the sequence;
training a second machine learning model based on the first set of outputs of wherein the second machine learning model is a combiner model trained to output a plurality of disease predictor values based on the first set of outputs, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types, wherein each disease type of the plurality of disease types corresponds to a predetermined group of diseases;
applying the plurality of first machine learning models to features extracted from data including animal characteristics of at least one animal in order to generate a second set of outputs of the plurality of first machine learning models;
applying the second machine learning model to the second set of outputs of the plurality of first machine learning models in order to output the plurality of disease predictor values;
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.
15. The method of claim 14 , further comprising:
determining at least one disease prediction for the animal based on the results of the plurality of disease contraction simulations, wherein the at least one display element is generated based further on the at least one disease prediction.
16. The method of claim 15 , further comprising:
generating at least one insight based on the at least one disease prediction for the animal, wherein the at least one insight includes a likelihood of developing each of at least one disease at each of a plurality of stages of development of the animal.
17. The method of claim 16 , wherein the plurality of stages of development of the animal includes at least one of puppy, young, mature, and geriatric.
18. The method of claim 15 , further comprising:
generating at least one insight based on the at least one disease prediction for the animal, wherein the at least one insight includes at least one expected cost of health treatment for the animal.
19. The method of claim 14 , wherein the plurality of disease predictor values is a plurality of second disease predictor values, further comprising:
applying the plurality of first machine learning models to the features extracted from the data including the animal characteristics data of the at least one 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 the 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 trained using a training data set including training outputs for the plurality of first machine learning models.
20. The method of claim 14 , 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.
21. The method of claim 1 , further comprising:
determining a probability distribution for each of the plurality of disease types; and
creating a model of possible results for each of the plurality of disease types based on the probability distribution determined for the disease type, wherein the plurality of disease contraction simulations are run based on the model of possible results created for each of the plurality of disease types.
22. The system of claim 5 , wherein the system is further configured to:
determine a probability distribution for each of the plurality of disease types; and
create a model of possible results for each of the plurality of disease types based on the probability distribution determined for the disease type, wherein the plurality of disease contraction simulations are run based on the model of possible results created for each of the plurality of disease types.
23. The method of claim 9 , further comprising:
determining a probability distribution for each of the plurality of disease types; and
creating a model of possible results for each of the plurality of disease types based on the probability distribution determined for the disease type, wherein the plurality of disease contraction simulations are run based on the model of possible results created for each of the plurality of disease types.
24. The method of claim 14 , further comprising:
determining a probability distribution for each of the plurality of disease types; and
creating a model of possible results for each of the plurality of disease types based on the probability distribution determined for the disease type, wherein the plurality of disease contraction simulations are run based on the model of possible results created for each of the plurality of disease types.