System and method for identifying one or more criteria from textual data
A system for identifying one or more criteria from textual data including at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a plurality of data, identify one or more sets of criteria from the plurality of data, classify each of the one or more sets of criteria using natural language processing module, compare the one or more sets of criteria and the individual profile based on the embeddings, generate a context-specific set of criteria as a function of the comparison between the one or more sets of criteria and the individual profile, and apply the context-specific set of criteria using a dynamic decision template to generate an outcome.
1. A system for identifying one or more criteria from textual data,
wherein the system comprises:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive a plurality of data;
identify one or more sets of criteria using a language processing module;
classify each of the one or more sets of criteria using the language processing module, wherein classifying each of the one or more sets of criteria comprises:
generating criteria embeddings from the one or more sets of criteria; and
generating profile embeddings from an individual profile;
compare the one or more sets of criteria and the individual profile based on the embeddings, wherein comparing the one or more sets of criteria and the individual profile comprises comparing the one or more sets of criteria and the individual profile across one or more axes comprising at least an axis chosen from a list consisting of a clinical axis, a temporal axis, a demographic axis, a treatment axis, a resource utilization axis, an outcome axis, a geographic axis, a behavioral axis, a social support axis, and a technological axis;
generate a context-specific set of criteria as a function of the comparison between the one or more sets of criteria and the individual profile; and
apply the context-specific set of criteria using a dynamic decision template to generate an outcome.
2. The system of claim 1 , wherein the at least a processor is further configured to display, at a display device, the context-specific set of criteria and the outcome.
3. The system of claim 2 , wherein the display device comprises a graphical user interface (GUI), wherein the GUI is updated based on user inputs and the context-specific set of criteria.
4. The system of claim 3 , wherein the GUI comprises a plurality of event handlers.
5. The system of claim 1 , wherein the at least a processor is further configured to store the context-specific set of criteria.
6. The system of claim 1 , wherein the at least a processor is configured to identify one or more sets of criteria using a clustering model and a classification model, wherein:
the clustering model identifies a natural grouping and informs the classification model; and
the classification model utilizes named entity recognition processes to identify and classify entities within text.
7. The system of claim 1 , wherein comparing the one or more sets of criteria and the individual profile across one or more axes comprises using a clustering model and a classification model, wherein:
the clustering model groups one or more axes based on inherent similarities and labels one or more resulting clusters based on the inherent similarities; and
the classification model is trained on the labeled one or more resulting clusters and is configured to predict cluster labels of new data.
8. The system of claim 1 , wherein generating a context-specific set of criteria is accomplished using a deep learning model, wherein generating a context-specific set of criteria using a deep learning model comprises:
applying a deep clustering algorithm that leverages the criteria embeddings and the profile embeddings to group the one or more sets of criteria and the individual profile into meaningful clusters;
train the deep learning model on the labeled clusters to classify new data, allowing for nuanced sentiment predictions based on learned features; and
iteratively update the deep learning model with new data to adapt clustering and classification models.
9. The system of claim 8 , wherein the deep learning model utilizes multi-task learning, wherein multi-task learning allows the deep learning model to learn shared representations that benefit both tasks of clustering and classifying the one or more sets of criteria and the individual profile.
10. A method for identifying one or more criteria from textual data,
wherein the method comprises:
receiving a plurality of data;
identifying one or more sets of criteria from the plurality of data, using a language processing module;
classifying each of the one or more sets of criteria using the language processing module, wherein classifying each of the one or more sets of criteria comprises:
generating criteria embeddings from the one or more sets of criteria; and
generating profile embeddings from an individual profile;
comparing the one or more sets of criteria and the individual profile based on the embeddings, wherein comparing the one or more sets of criteria and the individual profile comprises comparing the one or more sets of criteria and the individual profile across one or more axes comprising at least an axis chosen from a list consisting of a clinical axis, a temporal axis, a demographic axis, a treatment axis, a resource utilization axis, an outcome axis, a geographic axis, a behavioral axis, a social support axis, and a technological axis;
generating a context-specific set of criteria as a function of the comparison between the one or more sets of criteria and the individual profile; and
applying the context-specific set of criteria using a dynamic decision template to generate an outcome.
11. The method of claim 10 , wherein the method further comprises displaying, at a display device, the context-specific set of criteria and the outcome.
12. The method of claim 11 , wherein the display device comprises a graphical user interface (GUI), wherein the GUI is updated based on user inputs and the context-specific set of criteria.
13. The method of claim 12 , wherein the GUI comprises a plurality of event handlers.
14. The method of claim 10 , wherein the method further comprises storing the context-specific set of criteria.
15. The method of claim 10 , wherein the method further comprises identifying one or more sets of criteria using a clustering model and a classification model, wherein:
the clustering model identifies a natural grouping and informs the classification model; and
the classification model utilizes named entity recognition processes to identify and classify entities within text.
16. The method of claim 10 , wherein comparing the one or more sets of criteria and the individual profile across one or more axes comprises using a clustering model and a classification model, wherein:
the clustering model groups one or more axes based on inherent similarities and labels one or more resulting clusters based on the inherent similarities; and
the classification model is trained on the labeled one or more resulting clusters and is configured to predict cluster labels of new data.
17. The method of claim 10 , wherein generating a context-specific set of criteria is accomplished using a deep learning model, wherein generating a context-specific set of criteria using a deep learning model comprises:
applying a deep clustering algorithm that leverages the criteria embeddings and the profile embeddings to group the one or more sets of criteria and the individual profile into meaningful clusters;
train the deep learning model on the labeled clusters to classify new data, allowing for nuanced sentiment predictions based on learned features; and
iteratively update the deep learning model with new data to adapt clustering and classification models.
18. The method of claim 17 , wherein the deep learning model utilizes multi-task learning, wherein multi-task learning allows the deep learning model to learn shared representations that benefit both tasks of clustering and classifying the one or more sets of criteria and the individual profile.