Word Embedding Entity Identification in Medical Text
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Solution Overview
Problem
Current methods for processing raw and unstructured medical text data require significant human intervention for labeling and annotation, and are limited in identifying diverse entities and custom entity types, making them inefficient and computationally intensive.
Innovation Solution
A computing system that employs word embeddings and character embeddings to automatically identify entities and custom entity types in medical text data using a knowledge base, dynamically expanding windows, and defining regions in vector spaces to determine the best representations of entities without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling and annotation methods are used for medical text data, then accuracy can be maintained, but processing time and computational resources increase significantly
Solution Approach 1:
The system enables automatic entity identification by allowing the computational model to autonomously process and classify medical entities without requiring manual human intervention for labeling and annotation, thus reducing processing time while maintaining accuracy through self-learning mechanisms
Solution Approach 2:
The patent replaces manual mechanical labeling processes with an automated computational system that uses word embeddings and machine learning algorithms to identify and classify medical entities, eliminating the need for human annotators while improving processing efficiency
2Adaptability or versatility
If traditional entity identification methods are used, then implementation is simpler, but the ability to identify diverse entities and custom entity types is limited
Solution Approach 1:
The computational system is designed to handle multiple entity types and custom entity types through a unified framework that uses word embeddings and contextual analysis, enabling it to adapt to diverse medical domains and entity categories without requiring separate specialized systems
Solution Approach 2:
The system adjusts its parameters and embedding dimensions dynamically to accommodate different entity types and medical domains, allowing it to identify diverse entities by changing its internal representation parameters rather than requiring fundamentally different identification mechanisms
3Productivity
If manual processing methods are used for medical text data, then computational resources are easier to manage, but processing efficiency decreases
Solution Approach 1:
The system applies partial processing by focusing computational resources on identifying only the most relevant entities and their immediate contextual relationships, rather than analyzing every aspect of the text, thus improving processing efficiency while managing computational resource consumption
Data Source
AI summary
Some embodiments provide a program that receives a set of words. The program further determines an embedding for a word in the set of words. The program also accesses a knowledge base to retrieve a plurality of entries. Each entry includes a text description of a concept. The program further determines, for each entry in the plurality of entries in the knowledge base, an embedding for the entry based on the text description of the concept. The program also calculates, for each entry in the plurality of entries in the knowledge base, a distance value between the embedding for the word in the set of words and the embedding for the entry. The program further determines an entry in the plurality of entries in the knowledge base having a text description of a concept that best represents the set of words based on the plurality of distance values.