Apparatus and method for responding to a user query by generating a data structure

1 . An apparatus for responding to a user query using a data structure, the apparatus comprising:

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 sets of note data, wherein the plurality of sets of note data comprises at least a temporal element;

analyze the plurality of sets of note data using a machine-learning module, wherein the machine-learning module comprises a large language model configured to receive the plurality of sets of note data as an input;

output, using the large language model, a cohort definition language data structure as a function of the analysis, wherein the cohort definition language data structure is output in a form of a decision tree wherein a first layer of the decision tree corresponds to a most recent timeline and subsequent layers of the decision tree reflect progressively earlier instances and wherein the cohort definition language data structure comprises a plurality of temporal criteria related to a time of treatment as nodes in the decision tree;

receive a user query datum;

generate a filtered datum as a function of the cohort definition language data structure and the user query datum wherein the filtered datum comprises a set of filtered temporal criteria related to the plurality of temporal criteria; and

retrieve real-time health record data from a note database as a function of the set of filtered temporal criteria.

2 . The apparatus of claim 1 , wherein analyzing the plurality of sets of note data comprises analyzing the plurality of sets of note data using a Contrastive Visual Representation Learning from Text (ConVIRT) model of the machine-learning module.

3 . The apparatus of claim 1 , wherein analyzing the plurality of sets of note data using the machine-learning module comprises:

generating language training data, wherein the language training data comprises exemplary note data and exemplary textual outputs;

training the large language model using the language training data; and

analyzing the plurality of sets of note data using the trained large language model.

4 . The apparatus of claim 3 , wherein analyzing the plurality of sets of note data using the machine-learning module comprises iteratively training the large language model as a function of previous iterations.

5 . The apparatus of claim 3 , wherein analyzing the plurality of sets of note data using the machine-learning module comprises:

generating cohort training data, wherein the cohort training data comprises correlations between exemplary note data and exemplary patient cohorts;

training a cohort classifier of the machine-learning module using the cohort training data; and

classifying the plurality of sets of note data into one or more patient cohorts using trained cohort classifier.

6 . The apparatus of claim 5 , wherein generating the filtered datum comprises:

receiving patient data; and

generating the filtered datum as a function of the patient data, the cohort definition language data structure, the user query datum, and the one or more patient cohorts.

7 . The apparatus of claim 1 , wherein the cohort definition language data structure comprises a JavaScript Object Notation (JSON) format.

8 . The apparatus of claim 1 , wherein generating the filtered datum comprises generating a subsequent filtered datum as a function of the filtered datum and a user request datum using the cohort definition language data structure.

9 . The apparatus of claim 1 , wherein the memory contains instructions further configuring the at least a processor to transmit the filtered datum to a remote device.

10 . A method for responding to a user query using a data structure, the method comprising:

receiving, using at least a processor, a plurality of sets of note data, wherein the plurality of sets of note data comprises at least a temporal element;

analyzing, using the at least a processor, the plurality of sets of note data using a machine-learning module, wherein the machine-learning module comprises a large language model configured to receive the plurality of sets of note data as an input;

outputting, using the at least a processor and the large language model, a cohort definition language data structure as a function of the analysis, wherein the cohort definition language data structure is output in a form of a decision tree wherein a first layer of the decision tree corresponds to a most recent timeline and subsequent layers of the decision tree reflect progressively earlier instances and wherein the cohort definition language data structure comprises a plurality of temporal criteria related to a time of treatment as nodes in the decision tree;

receiving, using the at least a processor, a user query datum;

generating, using the at least a processor, a filtered datum as a function of the cohort definition language data structure and the user query datum, wherein the filtered datum comprises a set of filtered temporal criteria related to the plurality of temporal criteria; and

configure the at least a processor to retrieve real-time health record data from a note database as a function of the set of filtered temporal criteria.

11 . The method of claim 10 , wherein analyzing the plurality of sets of note data comprises analyzing the plurality of sets of note data using a Contrastive Visual Representation Learning from Text (ConVIRT) model of the machine-learning module.

12 . The method of claim 10 , wherein analyzing the plurality of sets of note data using the machine-learning module comprises:

generating language training data, wherein the language training data comprises exemplary note data and exemplary textual outputs;

training the large language model using the language training data; and

analyzing the plurality of sets of note data using the trained large language model.

13 . The method of claim 12 , wherein analyzing the plurality of sets of note data using the machine-learning module comprises iteratively training the large language model as a function of previous iterations.

14 . The method of claim 12 , wherein analyzing the plurality of sets of note data using the machine-learning module comprises:

generating cohort training data, wherein the cohort training data comprises correlations between exemplary note data and exemplary patient cohorts;

training a cohort classifier of the machine-learning module using the cohort training data; and

classifying the plurality of sets of note data into one or more patient cohorts using trained cohort classifier.

15 . The method of claim 14 , wherein generating the filtered datum comprises:

receiving patient data; and

generating the filtered datum as a function of the patient data, the cohort definition language data structure, the user query datum, and the one or more patient cohorts.

16 . The method of claim 10 , wherein the cohort definition language data structure comprises a JavaScript Object Notation (JSON) format.

17 . The method of claim 10 , wherein generating the filtered datum comprises generating a subsequent filtered datum as a function of the filtered datum and a user request datum using the cohort definition language data structure.

18 . The method of claim 10 , further comprising:

transmitting, using the at least a processor, the filtered datum to a remote device.