Medical Record to Illustrative Image Translation for MS Prognosis
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Solution Overview
Problem
Current methods fail to effectively illustrate the hidden nervous system states and disease progression in multiple sclerosis, particularly for relapsing-remitting MS, as standard medical imaging cannot detect these states, leading to inadequate patient understanding and ad-hoc parameter adjustments in models like the leaking swimming pool model.
Innovation Solution
A dual neural machine translation system is employed to parameterize the leaking swimming pool model based on patient medical records, enabling the generation of illustrative medical images that depict disease dynamics and patient-specific pathophysiology, facilitating better patient understanding and model augmentation with additional data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard medical imaging is used to detect nervous system states, then imaging equipment and procedures are simple and widely available, but hidden nervous system states and disease progression in multiple sclerosis cannot be detected
Solution Approach 1:
The patent introduces an intermediary translation system that converts medical records into illustrative medical images. This intermediary process enables the visualization of hidden nervous system states by translating textual medical information into visual representations that reveal disease progression patterns not detectable by standard imaging alone.
Solution Approach 2:
The patent replaces traditional mechanical/physical imaging systems with an information-processing system using neural networks and translation engines. Instead of relying on physical imaging equipment to detect hidden states, the system uses computational methods to generate illustrative images from medical record data, substituting mechanical detection with intelligent data transformation.
2Reliability
If ad-hoc parameter adjustments are made in models like the leaking swimming pool model, then model flexibility is maintained, but model accuracy and reliability are insufficient
Solution Approach 1:
The patent implements feedback mechanisms where the translation engine continuously refines its parameterization of the leaking swimming pool model based on medical record data. The system uses iterative optimization where predicted prognosis records are converted to images, compared against actual patient outcomes, and used to adjust model parameters systematically rather than through ad-hoc changes.
Solution Approach 2:
The patent systematically changes model parameters through automated translation and optimization processes. Instead of manual ad-hoc adjustments, the system dynamically modifies parameters of the leaking swimming pool model based on patterns extracted from medical records, enabling accurate representation of disease progression while maintaining model structure.
3Loss of information
If neural networks and translation engines are used to generate illustrative medical images, then patient understanding and disease progression modeling are improved, but computational resource requirements and processing time increase
Solution Approach 1:
The patent performs preliminary processing of medical records by the translation engine to pre-compute and store illustrative medical images and extracted features. By preparing prognosis images and parameterizations in advance, the system reduces processing time during clinical consultations, as the heavy computational work of translating medical records to images is completed beforehand rather than in real-time.
Data Source
AI summary
A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions that are executed by the at least one processor and configure the at least one processor to implement a medical record to illustrative medical image translation engine. The medical record to illustrative medical image translation engine receives a medical record batch from storage for a patient and generates one or more predicted prognosis records based on the medical record batch using a neural network. The medical record to illustrative medical image translation engine converts the one or more predicted prognosis records to illustrative medical images using a first agent. The medical record to illustrative medical image translation engine generates a presentation of disease progression using the illustrative medical images and outputs the presentation to a user.