Deep Learning Encoder for Multi-Modal Patient Data Similarity
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Solution Overview
Problem
Existing methods for similarity search in healthcare data, particularly with high-dimensional and multi-modal data, face challenges in scalability and weighting disparities across different data types, leading to inadequate representation of predictive minority data sources.
Innovation Solution
Deep learning is employed to encode multi-modal patient data into compact signatures, allowing for comparison and prediction of clinical outcomes by generating joint data signatures with equal dimensionality across disparate data types, thereby improving similarity search efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical distance measures are employed to assess similarity, then similarity scores can be generated, but the minority data source is dominated by the majority data source (e.g., imaging data with 300 variables dominates over data with two variables)
Solution Approach 1:
The patent segments the multi-modal data into separate modality-specific datasets, each processed independently through dedicated neural network branches. This segmentation allows each data type to be normalized and encoded separately, preventing any single modality from dominating the similarity assessment while preserving the unique predictive information from minority data sources.
Solution Approach 2:
The patent transforms the original high-dimensional data from multiple modalities into standardized low-dimensional embeddings through neural network encoding. This parameter transformation equalizes the contribution of each modality by mapping them to a common dimensional space, ensuring that minority data sources with fewer variables contribute equally to the final similarity score.
2Productivity
If similarity search-based information retrieval is used to leverage large volume of data, then information retrieval can be performed, but the method is not scalable to large amounts of data
Solution Approach 1:
The patent performs preliminary encoding of all multi-modal patient data into compact embeddings before the actual similarity search is needed. These pre-computed embeddings are stored in a database, enabling rapid retrieval during clinical practice without requiring real-time computation on the original high-dimensional data, thus improving scalability.
Solution Approach 2:
The patent creates compact computational copies (embeddings) of the original high-dimensional patient data. These embeddings preserve the essential predictive information while occupying minimal computational space, allowing the system to perform similarity searches on large datasets efficiently without processing the full original data during retrieval operations.
3Productivity
If deep learning is used to encode multi-modal data into compact signatures, then similarity search efficiency is improved, but the encoder requires training and computational resources
Solution Approach 1:
The patent develops a universal encoder architecture that can process multiple different data modalities (imaging, laboratory tests, clinical history) through a unified neural network framework. This multi-functional encoder learns to extract relevant features from diverse data types and produces standardized embeddings, eliminating the need for separate processing pipelines for each data type and reducing overall system complexity.
Data Source
AI summary
In order to compare high-dimensional, multi-modal data for a patient to data for other patients, deep learning is used to encode original, multi-modal data for a patient into a compact signature. The compact signature is compared to predetermined compact signatures generated for other patients, and similar predetermined compact signatures are identified based on the comparison. A clinical outcome may be predicted based on the similar predetermined compact signatures that are identified.