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Secure Integration of Electronic Health Data Using Advanced Machine Learning and Blockchain Technology | ||
| Health Management & Information Science | ||
| دوره 12، شماره 4، دی 2025، صفحه 274-280 اصل مقاله (283.1 K) | ||
| 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. | ||
| 2Department of Medicine, Sirjan School of Medical Sciences, Sirjan, Iran | ||
| 3Department of Occupational Health Engineering, Sirjan School of Medical Sciences, Sirjan, Iran. | ||
| چکیده | ||
| Introduction: Data integration and privacy preservation in electronic health records (EHRs) remain major challenges. This study combines advanced machine learning and blockchain to improve integration and security. Methods: Using a synthetic multicenter EHR dataset (patient records, visits, diagnoses, medications, observations, procedures), we evaluated an Irregular Fuzzy Cellular Automata (IFCA) model—which incorporates fuzzy-logic rules—against XGBoost and LightGBM. Preprocessing included complete anonymization and 98.5% missing-value imputation. Machine learning addressed data integration, inconsistency resolution, and classification; HL7-FHIR–like formats and a Hyperledger Fabric consortium blockchain evaluated secure data exchange and access control. Analyses used Python 3.10 and R 4.2. Results: Machine learning (data integrity & classification): IFCA achieved 92% accuracy (F1=0.90, AUC-ROC=0.92), outperforming XGBoost (89%) and LightGBM (90%); ANOVA indicated statistically significant differences (P<0.05). Blockchain & interoperability (security & exchange): data-exchange success was 94%, combined privacy/security score 95%, with 92% simulated attack prevention. Conclusion: The combined approach shows promise for EHR integration and privacy preservation. Validation on real multisite EHR data is recommended to confirm generalizability. | ||
تازه های تحقیق | ||
Mostafa Kashani (Google Scholar) Asma Zare (Google Scholar) | ||
| کلیدواژه‌ها | ||
| Electronic Health Records؛ Data Integration؛ Machine Learning؛ Blockchain؛ Fuzzy Logic | ||
| مراجع | ||
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