Record linkage

File:The Rochester Epidemiology Project (REP) medical records-linkage system

Record Linkage

Record linkage is a process used in data management and analysis to identify and merge records that refer to the same entity across different data sources. This process is crucial in various fields such as healthcare, social sciences, and commerce, where data from multiple sources need to be integrated to provide a comprehensive view of an entity.

Overview

Record linkage involves comparing records from different datasets to determine if they refer to the same entity. This process can be challenging due to variations in data entry, missing information, and differences in data formats. The goal is to accurately match records while minimizing false matches and missed matches.

Methods of Record Linkage

There are several methods used in record linkage, each with its own advantages and limitations:

Deterministic Linkage

Deterministic linkage, also known as rule-based linkage, uses predefined rules to match records. These rules are based on exact matches of key identifiers such as Social Security numbers, names, or dates of birth. While deterministic linkage is straightforward and easy to implement, it may not handle data entry errors or variations effectively.

Probabilistic Linkage

Probabilistic linkage uses statistical models to calculate the likelihood that two records refer to the same entity. This method considers the possibility of errors and variations in the data, assigning weights to different attributes based on their discriminative power. Probabilistic linkage is more flexible and can achieve higher accuracy than deterministic methods, especially in datasets with noisy or incomplete data.

Machine Learning Approaches

Recent advances in machine learning have introduced new methods for record linkage. These approaches use algorithms to learn patterns in the data and improve matching accuracy. Machine learning models can be trained on labeled datasets to recognize complex relationships between records, making them suitable for large and diverse datasets.

Challenges in Record Linkage

Record linkage faces several challenges, including:

• Data Quality: Inconsistent, incomplete, or erroneous data can hinder the linkage process.
• Scalability: Large datasets require efficient algorithms to perform linkage in a reasonable time frame.
• Privacy Concerns: Linking records from different sources may raise privacy issues, especially when dealing with sensitive information.
• Evaluation: Assessing the accuracy of record linkage is difficult without a gold standard dataset.

Applications of Record Linkage

Record linkage is used in various applications, including:

• Healthcare: Linking patient records from different hospitals to provide a complete medical history.
• Social Sciences: Combining survey data from different sources to enhance research studies.
• Commerce: Merging customer data from different platforms to improve marketing strategies.

Also see

• Data Integration
• Data Quality
• Entity Resolution
• Privacy-preserving Record Linkage