Dual Neural Machine Translation for Hidden Nervous System State Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods struggle to accurately predict and treat hidden nervous system states in patients with relapsing-remitting multiple sclerosis (RR-MS), as standard medical imaging cannot detect these states, making it difficult to assign therapeutic agents, dosages, and time courses for treatment.
Innovation Solution
A Dual Neural Machine Translation (d-NMT) system is employed to train a generative model using sequences of medical records and translate them into illustrative medical images, allowing for the optimization of patient-specific models that determine therapies, dosages, and time courses for treatment by leveraging mechanistic modeling and pathophysiological simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard medical imaging is used to detect nervous system states, then the imaging methods are simple and widely available, but they cannot detect hidden nervous system states
Solution Approach 1:
The patent introduces an intermediary AI system that translates between medical records and illustrative medical images. This intermediary generative model enables the detection of hidden nervous system states by creating synthetic images from electronic health record data, bridging the gap between simple record-keeping and complex diagnostic imaging capabilities.
Solution Approach 2:
The patent replaces traditional mechanical imaging systems with an AI-based generative model that uses machine learning algorithms to create illustrative medical images from textual medical records. This substitution allows detection of hidden states without requiring complex physical imaging hardware.
2Measurement precision
If AI systems are trained to translate medical records to medical images, then detection accuracy improves, but training data requirements and computational resources increase
Solution Approach 1:
The generative model is trained to be self-sufficient by learning to translate medical records into illustrative images without requiring paired training data. The system serves itself by using only medical record inputs to generate both the target images and the corresponding pathological state information, eliminating the need for large volumes of annotated training data.
3Reliability
If patient-specific models are optimized using dual neural machine translation, then treatment determination accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the complex treatment determination process into two distinct neural translation components: one translating medical records to illustrative images and another translating images back to medical records. This segmentation allows each component to be optimized independently while maintaining overall system reliability for treatment assignment.
Solution Approach 2:
The system performs preliminary translation of medical records into illustrative images before treatment determination. This preliminary action creates a visual representation of the patient's pathological state that can be more effectively analyzed for treatment selection, improving accuracy while managing computational complexity through staged processing.
Data Source
AI summary
Systems and methods that facilitate determining interaction between medications and the brain using a brain measure and a brain model. Hidden nervous system states are difficult to predict, diagnose, and treat with therapeutic medications. A Dual Neural Machine Translation (d-NMT) algorithmic system that utilizes sets of parameters for a relapsing-remitting MS model based on patient medical records and adjusts a method of parameterization to produce a model that can match patients' medical records and medical images. These parameters are can be used by a therapeutic determining model to recommend therapies, doses, and time courses accurately.