Multimodal Contrastive Learning for Cardiac Auscultation Diagnosis
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Solution Overview
Problem
Current intelligent stethoscopes for heart sound analysis face challenges in accuracy due to weak heart sound signals easily disturbed by external noise, and require large amounts of fine-grained annotation data, which is time-consuming and labor-intensive, limiting their effectiveness in providing reliable diagnosis and treatment plans.
Innovation Solution
An unsupervised deep learning method based on contrastive learning and multimodality is employed, which encodes text and audio data from cardiac auscultation into vectors, allowing for diagnosis and treatment predictions without the need for fine-grained annotation, using a system that includes text and audio encoders, a training module, and a prediction module to calculate distances between vectors and provide diagnosis results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional machine learning methods are used for heart sound analysis, then the system can provide automated diagnosis, but the accuracy is reduced due to weak heart sound signals being disturbed by external noise
Solution Approach 1:
The patent combines multiple data modalities including heart sound signals, electronic health record text data, and medical knowledge graph information into a unified analysis framework. This multimodal integration allows the system to cross-validate findings and reduce the impact of noise in individual modalities, thereby maintaining automated diagnosis while improving accuracy
Solution Approach 2:
The patent introduces contrastive learning as an intermediary mechanism that learns robust representations by distinguishing between positive pairs (matching heart sound and EHR text) and negative pairs (mismatched pairs). This intermediary learning process enables the system to filter out noise and focus on meaningful patterns, resolving the contradiction between automation and accuracy
2Reliability
If fine-grained annotation data is used for training, then the model can achieve better performance, but the annotation process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent implements self-supervised learning where the system automatically generates training signals from the data itself without requiring manual annotation. Contrastive learning uses the inherent structure in the data (matching and non-matching pairs) to create supervision signals automatically, eliminating the need for time-consuming fine-grained annotation while maintaining model performance
Solution Approach 2:
The patent performs preliminary contrastive learning training to pre-train the model with automatically generated supervision signals before fine-tuning. This preliminary action establishes robust feature representations that reduce the need for subsequent manual annotation, saving time while preserving model performance
3Measurement precision
If more annotation data is collected to improve diagnosis accuracy, then the model performance increases, but the complexity of data processing and management increases
Solution Approach 1:
The patent extracts only the essential contrasting features from the data (positive pairs indicating correct associations and negative pairs indicating incorrect associations) rather than processing all possible data combinations. This extraction approach maintains diagnostic accuracy by focusing on discriminative features while reducing data processing complexity
Data Source
AI summary
A method, computer program product, and computer system are provided for predicting treatment options based on cardiac auscultation data. Text data and audio data corresponding to cardiac auscultation associated with a patient is received. The text data and the audio data are encoded as respective text vectors and audio vectors. A distance between the text vectors and the audio vectors is calculated. Diagnosis results are determined by a machine learning model based on the calculated distance between the text vectors and the audio vectors.