Leveraging The Power Of AI And ML To Generate Synthetic Data For Clinical Trials
Speed is what we are after in the 21st century to help us solve problems. That is why we are always looking for better solutions that can offer us quick help. Of course, artificial intelligence and machine learning have been at the forefront of this, as they offer fast solutions.
Even when creating clinical trials and mock-ups, one of the biggest challenges is the time it takes to compile the data. Because of this, researchers have to use various machine learning libraries to perform difficult analyses. However, if you are also in this field and looking to generate synthetic data, here is your brief guide.
The Basics Of Synthetic Data
Before we move on to more challenging concepts, you need to understand the basics of synthetic data. That is because when clinical researchers talk about synthetic data, they are usually referring to the data that has been generated from an original dataset. The original data will typically reflect the measurements of the actual events.
However, it could contain information that would easily identify the people that were observed. Of course, that is a big problem because patient privacy is the most important thing to healthcare professionals. Researchers can generate synthetic data by applying an algorithm to the original dataset in such cases.
The algorithm will reveal the patterns captured in the original data, but it will not compromise the identities of the research subjects. It allows researchers to main patient confidentiality while ensuring that they get the health data they need.
Applications Of Synthetic Data In Clinical Trials
Synthetic data can mimic the conditions in real life, anonymize the research subject information, and simulate novel events. Because of this, there are a few potential applications of synthetic data in clinical trials. These include:
1. Speeding Up Early-Phase Trial Process
One of the top things that synthetic data can do is speed up the early-phase trial process. That is because when researchers request data, it can take weeks to process these queries and give them what they need. Slow data access is highly detrimental to…