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Kashani, Mostafa, Barzekar, Seddigheh, Zare, Asma. (1404). Secure Integration of Electronic Health Data Using Advanced Machine Learning and Blockchain Technology. سامانه مدیریت نشریات علمی, (), -. doi: 10.30476/jhmi.2025.108377.1309
Mostafa Kashani; Seddigheh Barzekar; Asma Zare. "Secure Integration of Electronic Health Data Using Advanced Machine Learning and Blockchain Technology". سامانه مدیریت نشریات علمی, , , 1404, -. doi: 10.30476/jhmi.2025.108377.1309
Kashani, Mostafa, Barzekar, Seddigheh, Zare, Asma. (1404). 'Secure Integration of Electronic Health Data Using Advanced Machine Learning and Blockchain Technology', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.30476/jhmi.2025.108377.1309
Kashani, Mostafa, Barzekar, Seddigheh, Zare, Asma. Secure Integration of Electronic Health Data Using Advanced Machine Learning and Blockchain Technology. سامانه مدیریت نشریات علمی, 1404; (): -. doi: 10.30476/jhmi.2025.108377.1309

Secure Integration of Electronic Health Data Using Advanced Machine Learning and Blockchain Technology

Health Management & Information Science
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 19 مهر 1404
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.30476/jhmi.2025.108377.1309
نویسندگان
Mostafa Kashani1؛ Seddigheh Barzekar2؛ Asma Zare* 3
1Department of Health Information Technology, Sirjan School of Medical Sciences, Sirjan, Iran.
2Sirjan School of Medical Sciences, Sirjan, Iran.
3Department of Occupational Health Engineering, Sirjan School of Medical Sciences, Sirjan, Iran.
چکیده
Background : Data integration and privacy preservation in electronic health record (EHR) systems are critical challenges in modern healthcare. This study explores innovative solutions for managing heterogeneous data and enhancing security in EHRs using advanced machine learning models and blockchain technology.
Methods: A synthetic dataset comprising patient records, visits, diagnoses, medications, clinical observations, and procedures was used to evaluate the Irregular Fuzzy Cellular Automata (IFCA) model against XGBoost and LightGBM. Data preprocessing included complete anonymization and 98.5% imputation of missing values. Data exchange standards like HL7 FHIR and blockchain technology were employed to improve integration and security. Analyses were conducted using Python 3.10 and R version 4.2, aligning with CONSORT-AI and STROBE standards.
Results: IFCA outperformed XGBoost (89% accuracy) and LightGBM (90% accuracy) with 92% accuracy, an F1-score of 0.90, and an AUC-ROC of 0.92. A 94% data exchange success rate and 95% security were achieved using blockchain and privacy-preserving methods. ANOVA tests (p<0.05) confirmed significant differences.
Conclusion: This study provides effective solutions for data integration and privacy preservation in EHR systems. Future research should focus on real-world datasets and scalable methods.
کلیدواژه‌ها
Electronic Health Records؛ Data Integration؛ Machine Learning؛ Blockchain؛ Fuzzy Logic
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