Cross-platform deduplication of athletes’ medical cases considering data integrity

Abstract The heterogeneity of Electronic Health Record (EHR) systems leads to significant differences in data formats and structures across different platforms. This issue becomes particularly prominent for specific populations requiring frequent cross-institutional medical services, such as professional athletes. As athletes often receive treatment from multiple healthcare providers, redundant data entry across systems is common, and data updates frequently fail to synchronize in a timely manner. This not only wastes storage resources but may also result in incomplete clinical decision-making references. Addressing the amplified effect of this universal architectural challenge in specific scenarios, this study proposes a cross-platform deduplication and backup method for athletes’ medical records that considers data integrity. The distributed architecture is used to build the overall framework for cross platform deduplication backup of athletes’ medical cases. The Doc2Vec model is used to quantify the high-dimensional features of athletes’ medical case data on multiple platforms. The secure hash algorithm is used to generate the check code of athletes’ medical case data blocks. The circular order method is used to verify the integrity of the high-dimensional feature data of athletes’ medical cases, verify the integrity of the case file. Keccak algorithm is selected to calculate the fingerprint of the case data block. When the fingerprint block is matched successfully, it is determined that the new file and the stored file have the same data block, and the data is synchronized to all backup systems for deduplication to eliminate the risk of duplicate backup. The dual pointer directed acyclic graph of athletes’ medical cases after deduplication processing is used to realize cross platform deduplication backup of athletes’ medical cases. The experimental results show that the duplicate data deletion rate of multi platform athletes’ medical case deduplication backup using this method is higher than 0.99, and the backup speed is higher than 300Mbps.

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Publication Details

Journal
Discover Artificial Intelligence
Published
2025-09-29
DOI
https://doi.org/10.1007/s44163-025-00447-x
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
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article

Cross-platform deduplication of athletes’ medical cases considering data integrity

Zhen Zhao, Tian Shang
Discover Artificial Intelligence
Privacy-Preserving Technologies in Data
article

Cross-platform deduplication of athletes’ medical cases considering data integrity

Zhen Zhao, Tian Shang
article en

Abstract

Abstract The heterogeneity of Electronic Health Record (EHR) systems leads to significant differences in data formats and structures across different platforms. This issue becomes particularly prominent for specific populations requiring frequent cross-institutional medical services, such as professional athletes. As athletes often receive treatment from multiple healthcare providers, redundant data entry across systems is common, and data updates frequently fail to synchronize in a timely manner. This not only wastes storage resources but may also result in incomplete clinical decision-making references. Addressing the amplified effect of this universal architectural challenge in specific scenarios, this study proposes a cross-platform deduplication and backup method for athletes’ medical records that considers data integrity. The distributed architecture is used to build the overall framework for cross platform deduplication backup of athletes’ medical cases. The Doc2Vec model is used to quantify the high-dimensional features of athletes’ medical case data on multiple platforms. The secure hash algorithm is used to generate the check code of athletes’ medical case data blocks. The circular order method is used to verify the integrity of the high-dimensional feature data of athletes’ medical cases, verify the integrity of the case file. Keccak algorithm is selected to calculate the fingerprint of the case data block. When the fingerprint block is matched successfully, it is determined that the new file and the stored file have the same data block, and the data is synchronized to all backup systems for deduplication to eliminate the risk of duplicate backup. The dual pointer directed acyclic graph of athletes’ medical cases after deduplication processing is used to realize cross platform deduplication backup of athletes’ medical cases. The experimental results show that the duplicate data deletion rate of multi platform athletes’ medical case deduplication backup using this method is higher than 0.99, and the backup speed is higher than 300Mbps.

Discover Artificial IntelligenceVol. 5(1)
Wuhan University of Technology (CN), Wuchang University of Technology (CN), Hubei University of Technology (CN)
Openalex Percentile: Top 86%
Privacy-Preserving Technologies in Data
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