Medical AI System Text to Image Data Conversion
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Solution Overview
Problem
Existing medical AI systems face challenges in effectively processing and normalizing text-type medical data due to its ambiguity and varying lengths, which hinders accurate prediction and representation of patient conditions over time.
Innovation Solution
A medical AI learning system that converts text-type medical data into image-type data using a data extraction module, visualization module, pre-processing module, learning module, and prediction module, allowing for the generation of richer, more structured image data for equipment learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If text-type medical data is used directly for AI learning, then the system can process unstructured data, but the data normalization and structuring become difficult due to ambiguity and varying lengths
Solution Approach 1:
The patent introduces an intermediary conversion process that transforms text-type medical data into image-type data. This intermediary representation serves as a bridge between unstructured text and structured AI learning requirements, enabling the system to process text data while achieving proper normalization and structuring through the image conversion process.
Solution Approach 2:
The patent changes the fundamental parameter of data representation from text format to image format. By converting text-type medical data into image-type data with standardized dimensions and structures, the system transforms the data into a form that is both adaptable for processing and precisely structured for AI learning, resolving the contradiction between versatility and precision.
2Measurement precision
If natural language processing methods are used for text-type data, then word substitution with numbers can be achieved, but the data length normalization and meaning ambiguity remain problematic
Solution Approach 1:
The patent introduces image-type data as an intermediary representation between text processing and AI learning. This intermediary form preserves the semantic information from natural language processing while providing stable, normalized dimensions that solve the data length consistency problem. The image representation maintains measurement precision from word substitution while adding structural stability.
3Manufacturing precision
If standard terminology systems are used for medical data, then data standardization can be achieved, but coding efforts increase and uncoded data cannot be included
Solution Approach 1:
The patent creates a visual copy or representation of text-type medical data in the form of image-type data. This copying approach captures the essential information and standardization benefits without requiring complex coding systems. The image representation preserves data standardization quality while avoiding the complexity of extensive coding efforts and the limitation of excluding uncoded data.
4Loss of information
If text-based data is used to represent patient conditions over time, then the original information can be maintained, but temporal trends and disease progression become difficult to process
Solution Approach 1:
The patent transitions data from a one-dimensional text format to a two-dimensional image format. This dimensional change enables the representation of temporal trends and disease progression in a structured visual format that AI systems can process efficiently. The image representation preserves the information from text while adding spatial and temporal dimensions that improve processing productivity for analyzing patient conditions over time.
Data Source
AI summary
Disclosed is a medical equipment learning system which includes: a data extraction module configured to collect and then extract text-type data from medical data; a visualization module configured to generate image-type data as visualization data by using the text-type data extracted by the data extraction module; a pre-processing module configured to generate an input data set to execute equipment learning based on the visualization data; a learning module configured to execute equipment learning in the input data set generated by the pre-processing module; a prediction module configured to predict a disease when new image-type data is input based on the result learned in the learning module; and a storage module provided to store and check data of each module.