Cohort Definition Query Generation for Large-Scale Note Retrieval
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Solution Overview
Problem
Traditional methods of data retrieval and query response are inefficient as data volumes grow exponentially, leading to inefficiencies in responding to user queries.
Innovation Solution
An apparatus and method utilizing a processor and memory with a machine-learning module, including a large language model, to analyze note data, generate a cohort definition language data structure, and filter data based on user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods of data retrieval are used, then the system is simple to implement, but the response efficiency deteriorates as data volumes grow exponentially
Solution Approach 1:
The patent introduces a machine-learning module as an intermediary between the user query and the data repository. This module automatically generates and executes cohort definition language queries, acting as a mediator that translates natural language queries into efficient data retrieval operations, thereby improving response efficiency without requiring users to understand complex query languages
Solution Approach 2:
The system performs preliminary actions by pre-compiling and storing cohort definition language queries in a query repository. When a user submits a query, the system checks the repository for pre-compiled queries that match the user's intent, executing them directly without requiring real-time compilation. This preliminary preparation significantly speeds up query response time
2Adaptability or versatility
If traditional data retrieval methods are used, then the implementation is straightforward, but the ability to handle exponential data growth becomes inadequate
Solution Approach 1:
The patent changes the parameter of query representation from simple SQL statements to cohort definition language, which is specifically designed to handle complex medical data queries. This parameter change enables the system to adapt to exponential data growth by providing a more expressive and efficient query language that can handle complex filtering, grouping, and aggregation operations on large datasets
Solution Approach 2:
The system replaces manual query construction and execution with an automated machine-learning module that generates and executes cohort definition language queries. This substitution of mechanical manual processes with automated intelligent processes enables the system to handle exponentially growing data volumes without proportional increases in operational complexity
Data Source
AI summary
An apparatus and method for responding to a user query using a data structure are disclosed. The apparatus includes 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 includes 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, generate a cohort definition language data structure as a function of the analysis, receive a user query datum and generate a filtered datum as a function of the cohort definition language data structure and the user query datum.