Neural Point Process Event Prediction for Medical Decision Making
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Solution Overview
Problem
Existing event prediction systems in complex systems, such as cyber-physical and healthcare systems, face challenges in accurately predicting events using only historical event information or time series data, as they fail to effectively capture the complex causal interactions between events and system metrics, leading to inadequate preventative measures.
Innovation Solution
A method utilizing transformers and an aggregation network to encode multivariate time series and multi-type event sequences, generating a feature vector for event prediction, which considers both historical events and current system conditions, enabling probabilistic distribution modeling of next events and their timing, and implementing corrective actions to mitigate these events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only historical event information is used for prediction, then the system is simpler to operate, but prediction accuracy deteriorates due to failure to capture complex causal interactions
Solution Approach 1:
The patent combines multiple data sources (historical event information and multivariate time series data) into a unified prediction framework. The system merges event sequence encoding with time series encoding through an aggregation network, allowing the system to capture complex causal interactions between events and system metrics while maintaining operational simplicity through automated integrated processing
2Measurement precision
If only time series data is used for prediction, then the system structure is simpler, but prediction accuracy deteriorates due to inability to capture event causal interactions
Solution Approach 1:
The patent integrates time series data with historical event information through a dual-encoding architecture. Time series data is encoded using a transformer model, while event sequences are encoded separately, and both are aggregated through an aggregation network to produce comprehensive prediction features that capture both temporal patterns and causal event interactions
3Measurement precision
If comprehensive data integration is implemented, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing pipeline into distinct modular components: time series encoding module, event sequence encoding module, and aggregation module. Each component processes specific data types independently using specialized transformer models, then combines results through the aggregation network. This segmentation reduces overall processing complexity by allowing independent optimization and parallel execution of each module
Data Source
AI summary
Methods and systems for event prediction include encoding a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector. Event prediction is performed using the feature vector to identify a next event to occur within a system. A corrective action is performed responsive to the next event to prevent or mitigate an effect of the next event. The predicted next event can be used in a healthcare context to support decision making by medical professionals with respect to the treatment of a patient. The encoding may include machine learning models to implement the transformers and the aggregation network using deep learning.