Bayesian Network Likelihood Vectors for Medical NLU
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Solution Overview
Problem
Current natural language understanding (NLU) systems for medical data processing face challenges in accurately mapping free-text data to coded forms, with limited accuracy and capability to recognize a limited set of concepts, particularly in capturing semantics and bridging the gap between free-text and coded medical data.
Innovation Solution
A method using Bayesian networks with likelihood vectors to parse and encode medical concepts from free-text documents, enabling accurate recognition of a large number of concepts and simplifying the training process by applying parsed documents and likelihood vectors to the network, which automates token slotting and improves accuracy through probabilistic reasoning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NLU systems with semantic grammars are used, then the system can process natural language documents, but the accuracy is limited and the capability to recognize concepts is very limited
Solution Approach 1:
The patent transforms the traditional deterministic semantic grammar approach into a probabilistic framework by introducing likelihood vectors and Bayesian networks. This parameter change allows the system to represent uncertainty and variability in natural language, thereby improving both accuracy and concept recognition capability simultaneously.
Solution Approach 2:
The patent replaces the rigid mechanical rule-based semantic grammar system with a probabilistic Bayesian network system. This substitution enables the system to handle the inherent uncertainty in natural language more effectively, resolving the contradiction between limited accuracy and limited concept recognition capability.
2Ease of operation
If regularities in speech patterns are used to break sentences into grammatical parts, then the syntax of sentences can be elucidated, but the semantics of sentences cannot be consistently mapped
Solution Approach 1:
The patent introduces likelihood vectors as an intermediary between syntactic parsing and semantic mapping. These vectors serve as a bridge that carries probabilistic information from the syntactic structure to the semantic interpretation, enabling consistent semantics mapping while preserving syntax elucidation capabilities.
3Measurement precision
If a combination of semantic and syntactic techniques is used, then better natural language understanding is achieved, but the system complexity increases
Solution Approach 1:
The patent merges semantic and syntactic techniques into a unified probabilistic Bayesian network framework. By combining these approaches at the mathematical level rather than maintaining separate processing stages, the system achieves improved natural language understanding while managing complexity through integration rather than addition.
Data Source
AI summary
A method for natural language processing on a computing device is described. The computing device receives a free text document. The computing device parses the free text document for gross structure. The gross structure includes sections, paragraphs and sentences. The computing device determines an application of at least one knowledge base. The free text document is parsed for fine structure on the computing device. The fine structure includes sub-sentences. The computing device applies the parsed document and at least one likelihood vector to a Bayesian network. The computing device outputs meanings and probabilities.