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GASP: Graph-based Approximate Sequential Pattern Mining for Electronic Health Records.

Wenqin Dong¹, Eric W Lee², Vicki Stover Hertzberg²

¹Carnegie Mellon University.

Advances in Databases and Information Systems. ADBIS

|October 4, 2021

Summary

This summary is machine-generated.

This study introduces GASP, a novel graph-based method for approximate sequential pattern mining in healthcare data. GASP efficiently discovers multi-item event sequences, improving pattern accuracy and predictive power over existing methods.

Keywords:

Healthcare Data Sequential Pattern Mining

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Area of Science:

Health Informatics
Data Mining
Machine Learning

Background:

Electronic health records (EHRs) contain valuable sequential patient data.
Conventional sequential pattern mining is computationally intensive and sensitive to noise.
Existing approximate methods struggle with multi-item event sequences common in healthcare.

Purpose of the Study:

To propose GASP, a graph-based approximate sequential pattern mining approach.
To address limitations of existing methods in handling multi-item event sequences.
To improve the accuracy and computational efficiency of extracting frequent patterns from EHRs.

Main Methods:

Developed GASP, a graph-based approximate sequential pattern mining algorithm.
Compressed sequential EHR data into a concise graph structure.
Evaluated GASP on two healthcare datasets.

Main Results:

GASP effectively discovers frequent patterns in multi-item event sequences.
The graph-based compression offers significant computational benefits.
Empirical results show GASP outperforms existing approximate models in recoverability and predictive pattern extraction.

Conclusions:

GASP provides an efficient and accurate solution for mining complex sequential patterns in EHRs.
The method enhances the discovery of clinically relevant multi-item event sequences.
GASP represents a significant advancement in approximate sequential pattern mining for healthcare applications.