Deep Learning Architecture for Unstructured Medical Data Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models, such as traditional LSTM neural networks, are ineffective in processing the vast amounts of unstructured text data found in medical records, making it difficult to identify patient attributes efficiently.
Innovation Solution
A deep learning model architecture that parses large data sources into snippet representations, using an LSTM-based pipeline to learn latent snippet representations, and combines them for classification, with the aid of regular expressions to extract relevant text and an attention mechanism to generate summary vectors indicative of patient attribute probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional LSTM neural networks are used to analyze unstructured text data, then the model can process sequential information, but it becomes ineffective when processing the vast amounts of data in medical records
Solution Approach 1:
The patent segments the large unstructured medical record into multiple smaller snippets of text. Each snippet is processed independently by the LSTM model to extract relevant attributes. This segmentation approach allows the model to handle vast amounts of data effectively by breaking down the complex processing task into manageable units, thereby maintaining reliability while scaling to large volumes of text data
Solution Approach 2:
The patent introduces a new dimensional approach by organizing snippets in a hierarchical structure and using attention mechanisms to weigh their importance. Instead of processing all text linearly, the system creates a multi-dimensional representation where snippets are grouped, weighted, and aggregated across different levels of abstraction, enabling effective processing of large data volumes
2Loss of information
If the model processes all text in medical records, then comprehensive information extraction is possible, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent extracts only the most relevant snippets from medical records that are likely to contain patient attributes. By using preprocessing filters and relevance scoring, the system identifies and extracts key portions of text while discarding redundant information. This extraction approach maintains complete attribute identification while significantly reducing processing time by focusing computational resources on high-value snippets
Solution Approach 2:
The patent performs preliminary processing of medical records before main analysis, including text normalization, snippet identification, and relevance scoring. This preliminary action prepares the data in advance, organizing it into a format that enables faster subsequent processing while ensuring no relevant information is lost, thus balancing completeness with efficiency
Data Source
AI summary
A model-assisted system for determining probabilities associated with a patient attribute. The processor may be programmed to access a database storing an unstructured medical record associated with a patient and analyze the medical record to identify snippets of information associated with the patient attribute. The processor may generate, based on each snippet, a snippet vector comprising a plurality of snippet vector elements comprising weight values associated with at least one word included in the snippet. The processor may analyze the snippet vectors to generate a summary vector comprising a plurality of summary vector elements, wherein each of the plurality of summary vector elements is associated with a corresponding snippet vector element and is determined based on an analysis of the corresponding snippet vector element. The processor may further generate, based on the summary vector, at least one output indicative of a probability associated with the patient attribute.