AI-MED GENERATOR©

Realistic and Privacy-guarantee Synthetic Patient Data

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The demand for high-quality, individual-level data in medical and healthcare research is increasing. Electronic health records (EHRs) covering entire populations can generate RWE and serve various secondary purposes, such as hypothesis testing and methodological development.

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However, accessing these data presents challenges due to stringent privacy requirements. EHRs and clinical trial data contain highly sensitive information, making access both costly and time-consuming. Data privacy and protection regulations are significant barriers to utilizing these datasets for research.

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Anonymization, which involves removing potentially identifiable variables, is one approach to making data accessible. However, extensive anonymization can degrade data quality, rendering it less useful. For example, adding random noise to data reduces precision and leads to larger confidence intervals.

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Additionally, until full anonymization is achieved, the collected sensitive data remains vulnerable to unauthorized access.

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One approach to addressing data privacy concerns is the use of synthetic data—artificially generated datasets that closely resemble the original data without containing any real individuals’ information. These datasets aim to maintain the statistical properties of the source data, such as distributions of continuous variables, proportions of categorical variables, correlations between variables, and other model parameters.

AI-MED GENERATOR© employs advanced AI-driven techniques to generate Synthetic Data that mirrors the statistical properties of real-world data (RWD), while preserving privacy and ensuring regulatory compliance. The process is designed to address the unique challenges of clinical trials, such as data scarcity, privacy risks, and the need for high-quality, analysable data.  

It supports fairness, privacy guarantee, and data augmentation across a variety of tabular data modalities, including static datasets, regular and irregular time series, censored data, multi-source datasets, composite data, and more.

1. Data Preparation

The process begins with the collection and preprocessing of real clinical trial data. This step includes:

  • Data Cleaning: Removing errors, inconsistencies, and missing values.
  • Profiling: Analysing data distributions, outliers, and imbalances to understand the dataset’s characteristics.
  • Balancing: Addressing imbalances in data (e.g., underrepresented patient groups) to improve the utility of synthetic data.
2. Model Training

AI-MED GENERATOR© uses Generative Adversarial Networks (GANs), a cutting-edge machine learning technique, to learn the underlying patterns and structures of the real dataset. GANs consist of two neural networks:

  • A generator that creates synthetic data.
  • A discriminator that evaluates how well the synthetic data mimics the real data.
  • This adversarial process iterates until the synthetic data is indistinguishable from the original in terms of statistical fidelity.
3. Synthetic Data Generation

Once trained, the GAN model generates synthetic datasets. These datasets:

  • Reflect the distributions, correlations, and relationships found in the original data.
  • Do not replicate actual patient records, ensuring no direct re-identification risks.
4. Privacy Risk Assessment

To comply with privacy regulations like GDPR and HIPAA, AI-MED GENERATOR© performs rigorous privacy checks:

  • Re-identification Risk Testing: Ensures no synthetic record can be linked back to an individual in the real dataset.
  • Outlier Detection: Identifies and mitigates the presence of rare or unique patterns that could lead to indirect identification.
5. Data Validation

The synthetic data is validated for:

  • Utility: Ensuring it is suitable for downstream analysis, such as training machine learning models.
  • Accuracy: Verifying that the synthetic data accurately reflects the real-world dataset without overfitting.
  • Compliance: Confirming adherence to regulatory standards and ethical guidelines for clinical trial data.
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Clinical Data Augmentation and Enhancement

  • Synthetic data can be used to supplement small datasets, especially in rare diseases or specialized studies where patient recruitment is challenging.
  • By generating realistic synthetic patient profiles, researchers can increase the dataset size, making statistical analyses more robust and reliable.
  • Synthetic data can help create balanced datasets, which is especially important in scenarios where some subgroups are underrepresented.
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Accelerating Feasibility Studies and Trial Designnt

  • Synthetic data allows researchers to simulate different scenarios and predict outcomes before conducting actual trials, optimizing trial design and protocol.

  • Researchers can test different variables, such as dosages or patient demographics, in synthetic datasets to identify the most promising approaches, which improves efficiency and reduces trial-and-error in actual clinical trials.

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Avoiding Privacy and Compliance Restrictions

  • Using Synthetic Data in place of real patient data helps protect patient privacy, as synthetic data does not contain personally identifiable information.
  • Synthetic Data can be shared across institutions, partners, or countries without risking privacy breaches, as the data is inherently anonymized and doesn’t correspond to actual individuals.
  • Characteristics of the original data, including missing values and patterns, are replicated depending on the method chosen to generate the synthetic data.

All Our Services

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GenAI-Driven Synthetic Control Arm

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GenAI-Driven RWD Acquisition & Analysis

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Target Trial Emulation for Drug Repurposing

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Realistic and Privacy-guarantee Synthetic Patient Data

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Clinical Trials Simulation in Silico

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Patient’s Data De-Identifier

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GenAI-Driven Remote Verbal Consent

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info@aimedtrial.com

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