Actor-Critic Model for Medical Record Classification
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Solution Overview
Problem
Current natural language processing techniques struggle to efficiently classify primary and secondary diagnoses in large, complex medical records due to high memory and processing demands, making it impractical for widespread implementation.
Innovation Solution
An actor-critic deep learning model processes medical records in segments, aggregating information into a vector representation to determine relevant information and classify documents, reducing memory and processing requirements by iteratively updating and refining the vector representation based on feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing techniques process entire large medical documents, then classification accuracy is improved, but memory and processing time requirements increase significantly
Solution Approach 1:
The patent divides large medical documents into smaller segments or chunks that can be processed individually. The system processes these segments sequentially, maintaining a compressed representation of the document state in memory at any given time, thereby reducing memory requirements while still achieving accurate classification through cumulative processing of all segments.
Solution Approach 2:
The patent introduces an intermediary compressed representation (such as a vector or summary) that mediates between the full document and the classification process. This intermediary structure allows the system to process large documents without loading the entire document into memory simultaneously, as only the compressed representation needs to be maintained in memory during processing.
2Measurement precision
If natural language processing techniques process entire large medical documents, then classification accuracy is improved, but processing time increases significantly
Solution Approach 1:
The patent segments large medical documents into smaller chunks that can be processed more quickly and efficiently. By processing segments sequentially rather than loading and processing the entire document at once, the system reduces overall processing time while maintaining classification accuracy through the accumulation of information from all segments.
Solution Approach 2:
The patent performs preliminary processing of document segments to create compressed representations before final classification. This preliminary action of creating summaries or vector representations of segments allows for faster subsequent processing and classification, as the system works with condensed information rather than raw text throughout the entire process.
3Quantity of substance
If only portions of documents are processed at a given time, then memory and processing resources are reduced, but data loss or omission of pertinent semantic information may occur
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously evaluates the compressed representation against classification criteria and adjusts its processing accordingly. This feedback loop ensures that pertinent semantic information is retained and processed, as the system can identify when additional information is needed and return to process relevant segments again, preventing information loss while maintaining low memory usage.
Solution Approach 2:
The patent uses an intermediary compressed representation that preserves essential semantic information from processed segments. This intermediary structure is designed to maintain the critical semantic content needed for accurate classification while occupying minimal memory space, thus preventing information loss despite processing only portions of the document at a time.
Data Source
AI summary
A method, apparatus and computer program product are provided for implementing an actor-critic model for processing a large document. The document is processed in smaller portions, and the information is accumulated or aggregated in a vector representation of the document. The vector representation may be reconfigured with each time step so that the vector representation includes an estimate of the most relevant information from the document processed thus far. The vector representation may be used to classify the document, such as by determining a primary and/or secondary diagnosis in a medical record. Feedback from the critic is provided in the form of a reward signal to the actor, which fine tunes its parameters and processes the different portions of the document. The actor-critic model may be trained with training data including documents and their known associated classifications.