Patient-centric synthetic data generation: a new methodology for Chronic Kidney Disease
Access to medical data is often restricted due to privacy and security policies. Synthetic data generation from real data is a widely adopted technique to address these limitations. This research presents a patient-centric methodology for generating synthetic data, specifically designed for patients...
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| Autores principales: | , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/176195 |
| Aporte de: |
| Sumario: | Access to medical data is often restricted due to privacy and security policies. Synthetic data generation from real data is a widely adopted technique to address these limitations. This research presents a patient-centric methodology for generating synthetic data, specifically designed for patients diagnosed with Chronic Kidney Disease (CKD). The key advantage of this proposal is its explainability and the traceability of the results, as it relies on statistics and data analysis rather than AI algorithms. The MIMIC-III clinical dataset serves as the foundation for generating synthetic patients in this study. This article details the data preprocessing and filtering applied to this dataset. Subsequently, synthetic data for CKD patients is generated using the proposed methodology. A comparison is then conducted between the synthetic data and the real data. Additionally, the synthetic data is compared with results obtained using the AI algorithm known as SMOTE. Generally, the metrics for the synthetic data generated by SMOTE are slightly superior. However, the results obtained with the proposed methodology exhibit minimal deviations from the MIMIC data across most CKD stages. |
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