Synthetic data refers to artificially generated data that mimics the statistical properties of real-world data. Synthetic data is created using computer algorithms, rather than being collected through observation or measurement. It is often used in situations where real data is scarce or cannot be easily accessed due to privacy concerns or other limitations.
The synthetic data generation process involves creating data that is statistically similar to the real data, but does not contain any identifiable or sensitive information. This can be done by using various statistical and machine learning techniques to generate data that follows the same patterns and distributions as the real data.
Synthetic data can be useful in a wide range of applications, such as training machine learning models, testing software systems, and conducting research studies. It can also help protect the privacy of individuals by allowing organizations to work with data that has been anonymized or de-identified.
How can synthetic data be used to train machine learning?
Synthetic data can be used to train machine learning models in a variety of ways. Here are some of the most common methods:
Synthetic data can be used to augment existing datasets by creating additional samples that are similar to the original data. For example, in image classification, synthetic data can be generated by adding noise, flipping, or rotating the images.
Synthetic data can be used to balance imbalanced datasets, where one class has significantly fewer samples than another. Synthetic data can be generated for the minority class to increase its representation in the dataset.
Synthetic data can be used to protect the privacy of sensitive information. Instead of using real data, synthetic data can be generated that has similar statistical properties as the real data but does not contain any identifiable information.
Synthetic data can be used to simulate scenarios that may be difficult or costly to replicate in the real world. For example, in autonomous vehicle development, synthetic data can be generated to train the vehicle’s AI to navigate in various environments.
Synthetic data can be used to transfer knowledge from one domain to another. For example, synthetic data generated from a simulation can be used to train a machine learning model that can then be fine-tuned on real data.
In all these cases, synthetic data can help improve the performance and robustness of machine learning models by providing additional data that can be used to train and test them.
Synthetic data can be used in medical research
Synthetic data can be used in medical research to address various challenges related to privacy, data availability, and ethical concerns. Here are some examples of how synthetic data can be used in medical research:
Protecting patient privacy
Synthetic data can be used to create anonymized datasets that protect patient privacy. Researchers can use these datasets to train machine learning models without compromising patient confidentiality. For example, synthetic data can be used to simulate medical images that are similar to real images but do not contain any identifiable information.
Improving rare disease research
Synthetic data can be used to create larger datasets for rare diseases, where real data may be limited. Synthetic data can be generated to mimic the statistical properties of real data, which can help improve the accuracy and generalizability of machine learning models.
Synthetic data can be used to simulate the effects of potential drugs on biological systems. Researchers can use synthetic data to generate models of drug-target interactions, which can help identify potential drug candidates and optimize drug design.
Synthetic data can be used to train machine learning models for medical imaging applications. For example, synthetic data can be used to generate training data for the segmentation of brain tumors or the detection of pulmonary embolisms.
Clinical trial design
Synthetic data can be used to simulate the results of clinical trials, which can help researchers design more efficient and effective studies. Synthetic data can be used to generate models of disease progression, treatment outcomes, and adverse events.
OSRC believes that synthetic data can provide a valuable tool for medical research, enabling researchers to tackle difficult problems related to data availability, privacy, and ethical concerns.
OSRC is determined to encourage research in synthetic and augmented data. Examples of publications by OSRC and its partners:
Algorithms to anonymize structured medical and healthcare data: A systematic review
Utility Metrics for Evaluating Synthetic Health Data Generation Methods: Validation Study
OSRC provides great opportunity for researchers because of its vast global network and its involvement in low-and-middle income countries.