Time-Point Guided Intervention Response Extraction from Clinical Data
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Solution Overview
Problem
Conventional computer information extraction systems are unable to effectively extract intervention responses from clinical data, particularly for conditions like solid tumors, due to the unavailability of radiographic images in electronic health records and the inability to identify datasets containing intervention response information, leading to inefficient processing of large amounts of irrelevant data.
Innovation Solution
An intervention response extraction system that uses machine learning models to process imaging-related data to determine time points when intervention responses were determined, identifies relevant datasets such as clinical visit notes, and extracts responses using trained models to predict intervention outcomes based on text analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional computer information extraction systems process all clinical data to extract intervention responses, then they can potentially find relevant information, but they waste computational resources processing large amounts of irrelevant data and cannot effectively identify datasets containing intervention response information
Solution Approach 1:
The system performs preliminary actions by training machine learning models offline to predict time points when intervention responses were determined and to identify datasets containing such responses. During actual extraction, these pre-trained models quickly filter and guide the processing of clinical data, avoiding the need to process all data from scratch and significantly reducing computational resources while improving extraction accuracy.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between the raw clinical data and the extraction process. These models act as mediators that predict time points and identify relevant datasets, enabling the system to efficiently locate intervention response information without processing all clinical data directly, thus resolving the contradiction between extraction reliability and computational resource usage.
2Measurement precision
If the system processes imaging related data to determine time points and identifies relevant datasets using machine learning models, then extraction accuracy improves, but the system complexity increases
Solution Approach 1:
The system segments the complex extraction task into distinct components: a time point determination module that predicts when intervention responses were determined, and a dataset identification module that locates relevant datasets. Each module uses specialized machine learning models trained for its specific function, improving overall accuracy while managing complexity through modular design where each segment handles a specific aspect of the extraction process.
3Reliability
If the system extracts intervention responses from multiple dataset collections using trained machine learning models, then extraction reliability improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on extensive datasets to learn patterns of intervention responses. During actual extraction, these pre-trained models rapidly process multiple dataset collections with high reliability. The offline training phase shifts the computational burden away from runtime processing, enabling fast and reliable extraction even when analyzing multiple datasets.
Data Source
AI summary
Described herein are techniques of automatically extracting intervention responses of a subject from data associated with the subject. The system automatically determines time points (e.g., dates) indicating periods in which intervention responses were determined for subjects, and then uses the time points to identify datasets from which to extract intervention responses. The system extracts intervention responses of the subject from the identified datasets.