Medical Transcript Concept Linking for Structured Data Extraction
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Solution Overview
Problem
Existing automated systems face challenges in accurately extracting structured data from conversational text due to variations in vocabulary, sentence structure, medically irrelevant information, noise, and contextually assumed information, making it difficult to link relevant pieces of information across a conversation.
Innovation Solution
A system and method that utilizes a machine learning model to identify and combine transcript concepts with associated labels, determining valid concept combinations to generate structured data by filtering transcript text and leveraging contextual information to link distant pieces of information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated extraction systems process entire conversational transcripts, then complete information is captured, but computational resources are excessively consumed and processing efficiency decreases
Solution Approach 1:
The system extracts only relevant portions of the conversational transcript that contain medical concepts, rather than processing the entire transcript. This is achieved by identifying and isolating specific text segments that are relevant to structured data extraction, thereby reducing computational load while maintaining extraction accuracy.
Solution Approach 2:
The conversational transcript is segmented into multiple portions based on relevance to medical concepts. The system divides the transcript into relevant and irrelevant segments, processing only the relevant portions for structured data extraction. This segmentation approach maintains complete information capture while significantly improving processing efficiency.
2Ease of operation
If traditional extraction methods are used without concept combination, then simpler processing is performed, but relevant information scattered across the conversation cannot be linked
Solution Approach 1:
The system combines multiple extracted medical concepts to form concept combinations that represent complete structured data instances. By merging related concepts that appear at different locations in the conversation, the system recovers information that would otherwise be lost, while maintaining processing simplicity through systematic combination rules.
3Measurement precision
If all extracted concepts are combined to form structured data, then comprehensive data coverage is achieved, but computational resources are wasted on invalid combinations
Solution Approach 1:
The system performs preliminary filtering of concept combinations based on validity criteria before complete processing. By预先 identifying and eliminating obviously invalid combinations, the system reduces computational resource waste while maintaining comprehensive data coverage for potentially valid combinations.
Data Source
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AI summary
A system for determining structured data includes a processor configured to receive a plurality of medical concepts and a corresponding plurality of labels. The processor is further configured to receive a transcript of a conversation between a physician and a patient. The processor is further configured to determine a plurality of transcript concepts in the transcript based on the plurality of medical concepts. The processor is further configured to assign each transcript concept with the label associated with the corresponding medical concept. The processor is further configured to determine a plurality of concept combinations by combining the plurality of transcript concepts. The processor is further configured to determine, via a machine learning model, a combination label for each concept combination. The combination label is a valid label or invalid label. The processor is further configured to generate an output structured data based on the concept combinations having the valid label.