Medical Order Classification Using Global and Local Vector Search
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Solution Overview
Problem
Existing techniques for classifying medical orders using machine learning or fine-tuned large language models (LLMs) are computationally expensive, require separate models for each medical site, and necessitate continual re-training, leading to significant computational and regulatory burdens.
Innovation Solution
Implement a system that generates global and local vector representations of medical orders using non-machine-learning vectorization techniques or pre-trained encoders, searching an order-label database for semantically similar past orders to classify new orders without re-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning or fine-tuned LLMs are used to classify medical orders, then classification accuracy is improved, but computational cost and regulatory burden increase
Solution Approach 1:
The patent segments the medical order into multiple textual sections and generates separate local vector representations for each section, rather than processing the entire order as a single unit. This segmentation enables more efficient similarity search while maintaining classification accuracy by capturing localized semantic information.
Solution Approach 2:
The patent uses pre-trained encoders to generate vector representations without requiring fine-tuning or re-training of large language models. By copying the vectorization approach from pre-trained models and applying it to segmented text, the system achieves accurate classification with significantly reduced computational burden.
2Adaptability or versatility
If separate models are trained for each medical site, then local adaptation and accuracy are improved, but device complexity and maintenance burden increase
Solution Approach 1:
The patent employs a universal pre-trained encoder that can be applied across multiple medical sites without requiring separate models. The same encoder generates vector representations for all sites, and the system adapts to local characteristics through the historical order database and similarity search mechanism, eliminating the need for site-specific model training and management.
3Measurement precision
If continual re-training is performed to maintain accuracy, then classification performance is improved, but loss of time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by using pre-trained encoders that have already been trained on large datasets. These pre-trained models capture general medical language patterns, eliminating the need for continual re-training at each medical site. The system adapts to specific sites through similarity search against historical orders rather than through re-training.
4Device complexity
If global vector representations alone are used, then computational simplicity is maintained, but information completeness and classification accuracy decrease
Solution Approach 1:
The patent segments the medical order into multiple textual sections and generates both global vector representations (for the entire order) and local vector representations (for individual sections). This multi-level segmentation captures both overall semantic meaning and localized details, improving classification accuracy while maintaining computational efficiency through efficient similarity search algorithms.
Data Source
AI summary
Systems or techniques that facilitate global and local search-based classification of text are provided. In various embodiments, a system can access a new medical order associated with a medical patient. In various aspects, the system can compute: one or more global vector representations of the new medical order; and one or more local vector representations for respective ones or combinations of a set of textual sections that make up the new medical order, thereby yielding a set of local vector representations of the new medical order. In various instances, the system can identify a new classification label for the new medical order, based on searching an historical order-label database using both the set of global vector representations and the set of local vector representations.