Joint Representation Processing for Sparse Multimodal Medical Data
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Solution Overview
Problem
Existing medical data analysis systems struggle to integrate diverse and sparse patient data across multiple modalities, leading to incomplete and potentially misinterpreted diagnoses, and there is a need to leverage machine learning to enhance data utilization and completeness.
Innovation Solution
A method involving a machine learning model that generates joint representations of text and image data, allowing for the completion of missing information and correction of erroneous data, using specialized hardware like FPGAs and neural processing units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated machine learning models are used to integrate multi-modal patient data, then diagnostic completeness and accuracy are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex task of multi-modal data integration into separate processing components: an encoder that processes individual data modalities (images, text, tables) independently, a fusion module that combines their representations, and a decoder that generates diagnostic outputs. This modular architecture improves diagnostic accuracy while managing system complexity through functional decomposition.
Solution Approach 2:
The patent introduces a latent representation space as an intermediary between raw multi-modal patient data and diagnostic conclusions. The encoder transforms diverse input modalities into a unified latent space where relationships can be learned, and the decoder retrieves diagnostic information from this intermediate representation. This intermediary layer enables effective integration of heterogeneous data types while abstracting the complexity of direct multi-modal processing.
2Loss of information
If comprehensive multi-modal data collection is performed for all patients, then data completeness improves, but data sparsity and missing information persist due to selective testing
Solution Approach 1:
The system performs preliminary encoding of available patient data across multiple modalities into a unified latent representation before diagnosis is finalized. By pre-processing and integrating all available information (even if incomplete) into a comprehensive latent space, the system prepares the groundwork for accurate diagnosis while accommodating the reality of selective testing and missing data.
Solution Approach 2:
The model incorporates attention mechanisms that provide feedback during the encoding and decoding processes. The attention weights indicate which data modalities and features contribute most to the diagnosis, allowing the system to adaptively focus on relevant information even when data is sparse. This feedback mechanism enables the system to compensate for missing data by leveraging relationships learned from the available modalities.
3Ease of operation
If manual data integration by medical practitioners is used, then data interpretation flexibility is maintained, but time consumption and human error increase
Solution Approach 1:
The system enables self-service automated integration of multi-modal patient data through the encoder-decoder architecture. The model automatically encodes various data modalities, fuses their representations in the latent space, and decodes diagnostic conclusions without requiring manual intervention. This self-service capability significantly reduces time consumption while maintaining diagnostic quality through learned patterns from training data.
Solution Approach 2:
The patent replaces the mechanical process of manual data integration by medical practitioners with an automated machine learning system. The encoder-decoder model with attention mechanisms substitutes human cognitive processes for integrating and interpreting multi-modal data, eliminating time-consuming manual work while reducing human error through consistent, data-driven decision-making.
4Object-affected harmful factors
If selective testing is performed to reduce patient burden, then patient comfort improves, but data gaps and incomplete diagnostic information increase
Solution Approach 1:
The system merges multiple data modalities (medical images, electronic health records, lab results, genomic data) into a unified latent representation. By combining information from these diverse sources, the system compensates for data gaps created by selective testing. The fusion of complementary information from different modalities allows comprehensive diagnosis even when individual tests are omitted to reduce patient burden.
Solution Approach 2:
The patent transforms the problem of data sparsity by adding the dimension of multi-modality. Instead of relying on complete data within a single modality, the system leverages relationships across different modalities. The latent space captures cross-modal relationships, allowing the system to infer missing information from available data in other modalities, thereby filling data gaps without requiring additional tests from each category.
Data Source
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AI summary
A method for training a machine learning model that is able to establish links between data of different modalities by creating a joint representation. In particular, application of the method to medical data including electronic medical records and medical images and/or other medical data. The trained machine learning model can among others fulfil tasks such as autocompletion of incomplete data, detection of uncertain and/or spurious data, generation of probable data and other tasks.