Predictive models having decomposable hierarchical layers configured to generate interpretable results
A computer-implemented method for providing interpretable predictions from a machine learning model includes receiving a data structure that represents a hierarchical structure of a set of features (X) used by one or more predictive models to generate a set of predictions (Y). An interpretability model is built corresponding to the predictive models, by assigning an interpretability to each prediction Y i based on the hierarchical structure. Assigning the interpretability includes decomposing X into a plurality of partitions X j using the hierarchical structure, wherein X=U 1 N X j , N being the number of partitions. Further, each partition is decomposed into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained. A score is computed for each partition as a function of the predicted scores of the sub-partitions, wherein the predicted scores represent interactions between the sub-partitions. Further, an interpretation of a prediction is outputted.
1. A computer-implemented method for providing interpretable predictions from a machine learning model for determining whether a patient qualifies for a medial trial for a particular diagnosis, the method comprising:
receiving, by a processor of a computing device, a data structure that represents a hierarchical structure of a set of features (X) of that is used by one or more predictive models to generate a set of predictions (Y); and
building, by the processor, an interpretability model corresponding to the predictive models, by assigning an interpretability to each prediction Y i of the predictive model based on the hierarchical structure, wherein assigning the interpretability comprises:
decomposing, by the processor, the set of features (X) of the input data into a plurality of partitions X j using the hierarchical structure, wherein X=U 1 N X j , N being the number of partitions;
decomposing, by the processor, each partition from the partitions into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained; and
computing, by the processor, a score for each partition as a function of the predicted scores of the sub-partitions of said each partition, wherein the predicted scores represent interactions between the sub-partitions; and
outputting, by the processor, an interpretation of a prediction provided by the predictive model based on the interpretability model, wherein the interpretation represents a weightage of the one or more features used by the predictive model to generate the prediction,
wherein:
the method further comprises identifying, by the processor, a recommended action based on the predicted score for a selected feature,
the predicted score for the selected feature indicates that a patient qualifies for a medical trial for a particular diagnosis, and
the recommended action comprises:
presenting a notification to a user that the patient qualifies for the medial trial; and
upon a selection to proceed by the user, generating an email outlining a specific type of medical treatment available through the medical trial.
2. The computer-implemented method of claim 1 , wherein the partitions of features are mutually exclusive subsets of the input data.
3. The computer-implemented method of claim 1 further comprising generating an overall score for the input data based on the predicted score for each partition.
4. The computer-implemented method of claim 1 , wherein generating the hierarchical structure further comprises:
receiving, by the processor, industry data that is curated by one or more experts; and
generating, by the processor, the hierarchical structure using the industry data by constructing a tree model by parsing the industry data into the partitions of features and creating associations between the partitions.
5. The computer-implemented method of claim 1 , wherein each partition is associated with a corresponding interpretability model.
6. The computer-implemented method of claim 1 , wherein the interpretability model is an attention model.
7. A system comprising:
at least one memory storing computer-executable instructions; and
at least one processor configured to access the at least one memory and perform a method for providing interpretable predictions from a machine learning model for determining whether a patient qualifies for a medial trial for a particular diagnosis, the method comprising:
receiving, by a processor of a computing device, a data structure that represents a hierarchical structure of a set of features (X) used by one or more predictive models to generate a set of predictions (Y); and
building, by the processor, an interpretability model corresponding to the predictive models, by assigning an interpretability to each prediction Y i of the predictive model based on the hierarchical structure, wherein assigning the interpretability comprises:
decomposing, by the processor, the set of features (X) of the input data into a plurality of partitions X j using the hierarchical structure, wherein X=U 1 N X j , N being the number of partitions;
decomposing, by the processor, each partition from the partitions into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained; and
computing, by the processor, a score for each partition as a function of the predicted scores of the sub-partitions of said each partition, wherein the predicted scores represent interactions between the sub-partitions; and
outputting, by the processor, an interpretation of a prediction provided by the predictive model based on the interpretability model, wherein the interpretation represents a weightage of the one or more features used by the predictive model to generate the prediction,
wherein:
the method further comprises identifying, by the processor, a recommended action based on the predicted score for a selected feature,
the predicted score for the selected feature indicates that a patient qualifies for a medical trial for a particular diagnosis, and
the recommended action comprises:
presenting a notification to a user that the patient qualifies for the medial trial; and
upon a selection to proceed by the user, generating an email outlining a specific type of medical treatment available through the medical trial.
8. The system of claim 7 , wherein the partitions of features are mutually exclusive subsets of the input data.
9. The system of claim 7 , wherein the method further comprises generating an overall score for the input data based on the predicted score for each partition.
10. The system of claim 7 , wherein generating the hierarchical structure further comprises:
receiving industry data that is curated by one or more experts; and
generating the hierarchical structure using the industry data by constructing a tree model by parsing the industry data into the partitions of features and creating associations between the partitions.
11. The system of claim 7 , wherein each partition is associated with a corresponding interpretability model.
12. The system of claim 7 , wherein the interpretability model is an attention model.
13. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors to cause the one or more processors to perform a method for providing interpretable predictions from a machine learning model for determining whether a patient qualifies for a medial trial for a particular diagnosis comprising:
receiving, by a processor of a computing device, a data structure that represents a hierarchical structure of a set of features (X) used by one or more predictive models to generate a set of predictions (Y);
building, by the processor, an interpretability model corresponding to the predictive models, by assigning an interpretability to each prediction Y i of the predictive model based on the hierarchical structure, wherein assigning the interpretability comprises:
decomposing, by the processor, the set of features (X) of the input data into a plurality of partitions X j , using the hierarchical structure, wherein X=U 1 N X j , N being the number of partitions;
decomposing, by the processor, each partition from the partitions into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained; and
computing, by the processor, a score for each partition as a function of the predicted scores of the sub-partitions of said each partition, wherein the predicted scores represent interactions between the sub-partitions; and
outputting, by the processor, an interpretation of a prediction provided by the predictive model based on the interpretability model, wherein the interpretation represents a weightage of the one or more features used by the predictive model to generate the prediction,
wherein:
the method further comprises identifying, by the processor, a recommended action based on the predicted score for a selected feature,
the predicted score for the selected feature indicates that a patient qualifies for a medical trial for a particular diagnosis, and
the recommended action comprises:
presenting a notification to a user that the patient qualifies for the medial trial; and
upon a selection to proceed by the user, generating an email outlining a specific type of medical treatment available through the medical trial.
14. The computer program product of claim 13 , wherein the partitions of features are mutually exclusive subsets of the input data.
15. The computer program product of claim 13 , wherein the method further comprises generating an overall score for the input data based on the predicted score for each partition.
16. The computer program product of claim 13 , wherein generating the hierarchical structure further comprises:
receiving industry data that is curated by one or more experts; and
generating the hierarchical structure using the industry data by constructing a tree model by parsing the industry data into the partitions of features and creating associations between the partitions.
17. The computer program product of claim 13 , wherein each partition is associated with a corresponding interpretability model.
18. The computer program product of claim 13 , wherein the interpretability model is an attention model.