Edit Encoder Text Similarity for Early Alzheimer’s Detection
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Solution Overview
Problem
Current methods for detecting early-stage Alzheimer's disease and other health conditions that affect speech and memory are not sensitive enough to subtle changes in language use, often requiring invasive procedures and are impractical for widespread use.
Innovation Solution
A computer-implemented method for training a machine learning model to evaluate text similarity by pre-training an edit encoder to learn an edit-space representation, which encodes high-level information on the differences between a reference and candidate text, using generative conditioning and task-specific training to capture subtle language changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cognitive tests with manual scoring are used to detect early-stage Alzheimer's disease, then measurement precision may be improved, but device complexity and ease of operation deteriorate due to requiring significant qualified staff time
Solution Approach 1:
The patent replaces manual cognitive testing and scoring with an automated machine learning system that processes speech transcripts. The system substitutes human expert analysis with computational algorithms including edit encoders, sentence encoders, and similarity modeling that automatically evaluate language use patterns without requiring qualified staff time.
Solution Approach 2:
The system enables self-service by allowing automated processing of speech data without human intervention in the scoring process. The machine learning model independently performs text encoding, similarity calculation, and disease risk assessment, making the system autonomous and eliminating dependency on qualified personnel for scoring.
2Measurement precision
If invasive procedures like PET scans or lumbar punctures are used to detect early-stage Alzheimer's disease, then measurement precision is improved, but ease of operation and object-affected harmful factors worsen due to invasive nature and cost
Solution Approach 1:
The patent employs inexpensive, non-invasive speech transcript analysis instead of expensive invasive procedures. The system processes readily available speech data using computational models, eliminating the need for costly PET scans, lumbar punctures, or specialized imaging equipment, making screening accessible and harmless.
Solution Approach 2:
The system introduces speech language analysis as an intermediary between direct biological measurement and disease detection. Instead of directly measuring amyloid plaques or tau proteins through invasive means, the system uses speech patterns as an indirect but safe marker of neurodegenerative changes, providing detection without physical intrusion.
3Device complexity
If automated text alignment methods based on manually defined elements are used, then device complexity is reduced, but measurement precision deteriorates by missing subtle language changes indicative of early-stage disease
Solution Approach 1:
The patent fundamentally changes the parameters used for text comparison from discrete element matching to continuous semantic similarity. Instead of checking for presence/absence of predefined story elements, the system uses sentence encoders and edit encoders to capture nuanced linguistic patterns, semantic relationships, and subtle deviations in language use that indicate early cognitive impairment.
Solution Approach 2:
The system combines multiple encoding approaches (edit encoding, sentence encoding, semantic analysis) into a composite evaluation model. This multi-faceted approach integrates various linguistic features and patterns to achieve superior detection sensitivity, capturing both obvious and subtle language changes that single-method approaches would miss.
Data Source
AI summary
The invention relates to a computer implemented method of training a machine learning model to evaluate the similarity of a candidate text to a reference text for determining or monitoring a health condition, where the model takes a text comparison pair comprising a reference text and a candidate text, each comprising data encoding a text sequence, the method comprising: pre-training an edit encoder to learn to generate an edit-space representation of an input text comparison pair, where the edit-space representation encodes information for mapping the reference text to the candidate text, the edit encoder comprising a machine learning model; and performing task-specific training by adding a task-specific network layer and training the task-specific network layer to map an edit-space representation generated by the pre-trained edit encoder to an output associated with a health condition. Edit-space representations learned in this way are able to encode a greater range of changes in language use than known metrics used to evaluate machine translations.