Multimodal Transformer Fusion for Medical Rare Event Prediction
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Solution Overview
Problem
Current medical rare event prediction models struggle with handling missing values in structured data and fail to effectively utilize both structured and unstructured data modalities, leading to limited predictive accuracy.
Innovation Solution
A method that preprocesses structured category and numerical data to fill missing values, projects them into vectorized features using an Embedding module, and combines these with textual data processed by a pre-trained language model, employing a transformer model with a multi-head self-attention mechanism to perform weighted fusion and output probabilities of medical rare events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transformer models are applied to structured data, then modeling capability is improved, but handling of missing values becomes problematic
Solution Approach 1:
The patent applies preliminary action by performing missing value imputation before feeding structured data into the transformer model. Specifically, missing values in structured data are filled using interpolation methods or by using default values, and a mask matrix is pre-computed to track which values were imputed. This preliminary processing enables the transformer to handle missing data without compromising its modeling capability.
Solution Approach 2:
The patent introduces an intermediary approach by using a mask matrix as a mediator between the structured data and the transformer model. The mask matrix records the presence or absence of valid values, allowing the transformer to process data with missing values through attention mechanisms that weigh the importance of different features. This intermediary structure enables the model to maintain both modeling capability and reliability in handling missing values.
2Measurement precision
If deep learning models are used, then fitting capability is improved, but model interpretability deteriorates
Solution Approach 1:
The patent merges the strengths of different model types by combining transformer-based deep learning models with tree-based models (such as XGBoost or LightGBM) in an ensemble architecture. The transformer component provides strong fitting capability for capturing complex patterns in multimodal data, while the tree-based component provides interpretability through feature importance and decision paths. This merging allows the system to achieve both high accuracy and explainability.
Solution Approach 2:
The patent applies parameter changes by adjusting the complexity and depth of the transformer model parameters to balance fitting capability and interpretability. Specifically, the model uses a relatively small number of transformer layers and heads, with controlled embedding dimensions, to maintain simplicity while capturing essential patterns. Additionally, the model incorporates attention weight visualization and feature importance metrics that can be adjusted to provide different levels of interpretability based on operational needs.
3Device complexity
If single-modality structured data is used, then model simplicity is maintained, but predictive accuracy deteriorates
Solution Approach 1:
The patent implements universality by creating a multi-modal data processing framework that can handle structured data, unstructured text data, and other data types through a unified transformer architecture. The model uses a single transformer backbone that processes different data modalities through appropriate embedding layers and attention mechanisms, allowing the system to maintain architectural simplicity while achieving high predictive accuracy through comprehensive data utilization.
Solution Approach 2:
The patent applies composite materials by combining multiple data modalities (structured and unstructured data) into a composite input representation that is processed by the transformer model. The model integrates features from different data sources through concatenation and attention mechanisms, creating a rich representation that captures complementary information. This composite approach enables the model to maintain simplicity in its core architecture while achieving superior predictive accuracy through the synergistic combination of multiple data types.
Data Source
AI summary
A medical rare event prediction method, apparatus and storage medium of multimodal data includes: on the basis of the existing model of the transformer, by preprocessing the data, the absence of imputed data, semantic feature extraction on structured data simultaneously leveraging powerful modeling capabilities exhibited by the model of transformers in multiple domains such as natural language processing and computer vision, again by extracting unstructured data, that is, the semantic characteristics of the text data, the semantic characteristics of the structured data and the unstructured data are fused, the prediction of the medical rare event is performed based on the fused characteristics, and the prediction of the medical rare event of the multimodal data is achieved using the structured data and the unstructured data, and the prediction accuracy is improved.