Deep Learning Record Linking Model for Scalable Data Integration
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Solution Overview
Problem
Current systems face challenges in linking records across different databases, particularly in identifying and matching records associated with the same individual, due to variations in data quality, format, and entry errors, which complicates data integration and sharing across organizations.
Innovation Solution
A deep learning model is employed to link records from various databases by using a deep neural network with fully connected layers, which accounts for phonetic characterization, name changes, and other factors, and applies similarity scores to identify matching records, allowing for probabilistic record linkage and online learning based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional record linkage methodologies are used, then the system can identify matching records, but the accuracy is reduced due to data quality variations, format differences, and entry errors
Solution Approach 1:
The patent transforms record linkage from exact matching to probabilistic similarity scoring by changing the matching parameters. Instead of requiring exact field equality, the system computes similarity scores (0-1 scale) for each field pair using algorithms like Levenshtein distance for text similarity and handles phonetic variations, allowing accurate matching despite data quality issues and entry errors
Solution Approach 2:
The patent introduces a deep learning model as an intermediary between raw record data and matching decisions. This neural network model processes extracted features from record pairs, learns optimal weighting of different fields, and outputs probabilistic similarity scores, mediating the complex task of handling format variations and data quality issues
2Reliability
If pairwise comparison of all records is performed, then complete record linkage is achieved, but the computational complexity increases significantly with large datasets
Solution Approach 1:
The patent segments the record linkage process into distinct phases: blocking (grouping records by key attributes), pairwise comparison (only within blocks), and clustering (grouping matched records). This segmentation reduces the computational complexity from O(n²) global comparison to manageable local comparisons within blocks, while maintaining reliability through subsequent clustering that ensures complete linkage
3Measurement precision
If a deep learning model is used for record linkage, then the accuracy and adaptability improve, but the model training and computation time increase
Solution Approach 1:
The patent performs preliminary actions by pre-training the deep learning model on synthetic and historical data before deployment. Feature extraction algorithms are pre-configured, and the model learns optimal field weightings in advance. During actual record linkage, the pre-trained model rapidly scores similarities without requiring real-time retraining, significantly reducing computation time while maintaining high accuracy
Solution Approach 2:
The patent implements dynamic adaptation by allowing the model to be retrained with user feedback and new data. The system dynamically adjusts to domain-specific patterns and data quality characteristics, improving accuracy over time. This dynamic retraining capability allows the system to adapt to changing data sources while managing computation time through incremental learning
Data Source
AI summary
Systems and methods are described for linking records from different databases. A search may be performed for each record of a received record set for similar records based on having similar field values. Recommended records of the record set may be assigned with the identified similar records to sub-groups. Pairs of records may be formed for each record of the sub-group, and comparative and identifying features may be extracted from each field of the pairs of records. Then, a trained model may be applied to the differences to determine a similarity score. Cluster identifiers may be applied to records within each sub-group having similarity scores greater than a predetermined threshold. In response to a query for a requested record, all records having the same cluster identifier may be output on a graphical interface, allowing users to observe linked records for a person in the different databases.