Different Synthetic Data In Health Care-Techniques Of Building Synthetic Data For Clinical Trial

4 min readApr 27, 2022
Data illustrations by Story set

Synthetic data is key for accelerating deep learning. Clinical Trial applications have a wide data distribution, and datasets from the real world may not fully encompass. Some of the underlying probabilities for risk evaluation during the new drug development process may not be available by using only real-world datasets.

Types Of Synthetic Data

The first step to generating synthetic data is to understand what type of synthetic data you need. There are three categories of synthetic data, and each of these will give you something unique. These include:

1. Fully Synthetic Data

This type of synthetic data does not derive from any original dataset. That means that all the variables are completely available, and the reidentification of a unit is next to impossible.

2. Partially Synthetic Data

Keep in mind that it mostly replaced sensitive data with synthetic data where the privacy of patients or other people is at stake. Such synthetic data heavily depends on the imputation model. Of course, that leads to a reduced model dependence.

However, that does not mean that some disclosure is possible that owes to the true value that remains within the original dataset. We know such data as partially synthetic data.

3. Hybrid Synthetic Data

Finally, we have the hybrid synthetic data, where the data is derived from synthetic and real-world data. It guarantees the integrity and relationship between other variables inside the dataset. The researchers investigate the underlying distribution of original data, and it forms the data point of the nearest neighbor.

Besides that, a near-record in the synthetic data is selected for each record of the real data. After that, they join the two of them to create the hybrid synthetic data.

How To Generate Synthetic Data Through Machine Learning And Artificial Intelligence

Now that you know the various synthetic data, you must understand the general strategies you must use to build synthetic data. These include:


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