Reinforcement Learning for Medical Sentence Simplification
Solution Overview
Problem
Current technologies fail to effectively simplify medical language by reducing run-on sentences into shorter, complete sentences that retain the intended meaning, particularly in healthcare settings, due to limitations in software programs requiring human input and supervised machine learning algorithms that lack consideration of logical context.
Innovation Solution
A logical sentence length reduction system utilizing a reinforcement learning system coupled with a real-time grammar engine and logic engine, which constrains edits within an operational window defined by the original sentence break and noun/verb phrases, to rebuild sentence fragments into grammatical and logical sentences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If supervised machine learning algorithms are used to simplify medical language, then automation is improved, but logical context consistency deteriorates
Solution Approach 1:
The patent implements a reinforcement learning system where the model receives feedback in the form of rewards or penalties based on whether its sentence simplification decisions maintain logical context consistency. This feedback mechanism allows the model to learn from errors and improve its ability to preserve anaphora relationships and logical flow while automating the simplification process.
Solution Approach 2:
The system performs self-service by automatically learning to maintain logical context consistency through reinforcement learning without requiring manual supervision for each simplification decision. The model autonomously improves its performance by learning from the rewards and penalties it receives, eliminating the need for continuous human oversight while maintaining reliability.
2Reliability
If manual rewriting of run-on sentences is performed, then logical context consistency is improved, but productivity deteriorates
Solution Approach 1:
The system performs preliminary action by pre-training the reinforcement learning model on large datasets of medical text before deployment. This preliminary training equips the model with the knowledge needed to maintain logical context consistency, allowing it to then automatically process sentences at high speed without requiring manual intervention for each case.
Solution Approach 2:
The patent replaces the mechanical system of manual sentence rewriting with an automated reinforcement learning system. This substitution maintains logical context consistency through intelligent learning while dramatically increasing productivity by processing sentences automatically without human labor constraints.
3Productivity
If sentence fragments are rebuilt without anaphora resolution, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent applies segmentation by breaking down the complex task of sentence simplification into smaller manageable components: identifying anaphora relationships, resolving references, and reconstructing sentences. The reinforcement learning model learns to handle each segment systematically, maintaining reference resolution accuracy while improving overall productivity through efficient processing of each segment.
Solution Approach 2:
The system introduces an intermediary anaphora resolution mechanism that acts as a bridge between sentence fragmentation and reconstruction. This intermediary step ensures that reference relationships are properly identified and resolved before sentences are rebuilt, maintaining measurement precision while enabling automated high-speed processing.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs language encoded on a computer storage medium for a logic sentence length reduction system whereby input text is modified to a logical and grammatical state using a reinforcement learning system with a real-time grammar engine and a real-time logic engine. The logic sentence length reduction system breaks sentences into fragments. An agent performs actions to each sentence fragment such that fragments are reconstructed into complete sentences. The real-time logic and real-time grammar engine are provided a reward for producing a grammatical and logical sentence. The reinforcement-learning agent optimizes a policy to reconstruct logical and grammatical sentences from sentence fragments maximizing future reward.