Creating A Patient Data Redundancy Detection Model using Deep Learning Methods

Prevandito Wahyu Widodo*, Nur Aini Rakhmawati

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Redundancy of patient data in healthcare facilities often arises because of double entry. Although Standard Operating Procedures (SOPs) have been enforced, patient conditions and situations during input tend to dominate input results. Entity matching is a potential solution for overcoming this problem. However, with the growth in data volume and the complexity of relationships between entities, entity matching often faces constraints related to the quality of results, time efficiency, and resources. As a solution, deep learning offers an alternative approach to entity matching, especially in situations where the data have a large size and a wide variety of entity representations, such as patient data. This study aimed to improve the validity of patient data by applying deep learning to entity matching. Four methods were planned for testing: SIF, RNN, Attention, and Hybrid. Although previous research has shown that the hybrid method significantly outperforms the other three methods in contexts with similar datasets, our findings demonstrate that the RNN method is more precise. From a dataset of 17,075 entries, we successfully identified 199 matching data pairs (positive labels), highlighting a significant initial imbalance. We employed an undersampling method to manage this imbalance. The results were promising, with an F1-score of 98.87.

Original languageEnglish
Title of host publication2024 7th International Conference on Informatics and Computational Sciences, ICICoS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages262-266
Number of pages5
ISBN (Electronic)9798350375886
DOIs
Publication statusPublished - 2024
Event7th International Conference on Informatics and Computational Sciences, ICICoS 2024 - Hybrid, Semarang, Indonesia
Duration: 17 Jul 202418 Jul 2024

Publication series

NameProceedings - International Conference on Informatics and Computational Sciences
ISSN (Print)2767-7087

Conference

Conference7th International Conference on Informatics and Computational Sciences, ICICoS 2024
Country/TerritoryIndonesia
CityHybrid, Semarang
Period17/07/2418/07/24

Keywords

  • Data Validity
  • Deep Learning
  • Entity Matching
  • Redundancy Data
  • patient

Fingerprint

Dive into the research topics of 'Creating A Patient Data Redundancy Detection Model using Deep Learning Methods'. Together they form a unique fingerprint.

Cite this