ECG Diagnosis Prompting With Retrieval-Augmented Zero-Shot LLMs
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Solution Overview
Problem
Existing large language models (LLMs) struggle with biased outputs and unreliable diagnoses when applied to highly technical tasks like ECG data analysis, posing risks for misinterpretation and misdiagnosis of health conditions.
Innovation Solution
A method and system that utilize retrieval-augmented generation (RAG) with a domain knowledge database to enhance ECG feature extraction and prompt engineering, enabling zero-shot inference for accurate health condition diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large language models are applied to ECG data analysis, then diagnosis speed is improved, but reliability of diagnosis deteriorates due to bias and unreliability in technical tasks
Solution Approach 1:
The patent introduces a retrieval-augmented generation system as an intermediary between the LLM and ECG data analysis. This system retrieves relevant medical knowledge and guidelines from a database to augment the LLM's reasoning process, thereby improving diagnosis reliability while maintaining the speed benefits of LLM-based analysis.
Solution Approach 2:
The system implements feedback mechanisms where the LLM's initial diagnosis is evaluated against retrieved medical knowledge and guidelines. If inconsistencies or low confidence are detected, the system retrieves additional relevant information and refines the diagnosis iteratively, ensuring both speed and reliability.
2Reliability
If traditional ECG analysis by trained experts is used, then diagnosis reliability is improved, but productivity deteriorates due to limited time availability
Solution Approach 1:
The system enables automated ECG analysis that performs expert-level diagnosis without requiring continuous human expert intervention. The retrieval-augmented LLM system independently analyzes ECG data, retrieves relevant knowledge, and generates diagnoses, thereby multiplying the effective productivity of expert knowledge while maintaining reliability.
3Adaptability or versatility
If LLMs are trained on vast data volumes for technical tasks, then adaptability is improved, but loss of information increases due to training data biases
Solution Approach 1:
The system performs preliminary retrieval of accurate, bias-free medical knowledge and guidelines from a curated database before the LLM generates its diagnosis. This preliminary action ensures that the LLM operates with access to reliable reference information, counteracting potential biases from its training data while maintaining adaptability to different ECG cases.
Data Source
AI summary
A method for diagnosing a health condition from electrocardiogram (ECG) data. The method may include obtaining the ECG data from an ECG machine and extracting a plurality of features from the ECG data resulting in raw extracted ECG features. The method may further include modifying the raw extracted ECG features to engineered ECG features based on a diagnosis guidance obtained from a database of domain knowledge using retrieval augmentation. The database of domain knowledge having been previously prepared and storing, at least, historical ECG data. The method may further include obtaining augmentation information from the database of domain knowledge using the engineered ECG features. The method may further include preparing a prompt that includes the engineered ECG features, the diagnosis guidance, and the augmentation information. The method may further include determining a health condition diagnosis based on the prompt using zero-shot inference with a large language model (LLM).