When it comes to clinical trials and all the work that entails, compiling data can quickly become a tedious challenge. To deconstruct the data and turn it into meaningful information, you must be able to use Machine Learning libraries to analyze and expose potential key insights.
Researchers in clinical trials tend to wait for long periods for the data they’ve collected to be thoroughly analyzed and transformed into useable information. This is time spent that could be used more effectively if they could get their hands on such data sooner.
This is where machine learning comes in. By utilizing the technology available through machine learning, researchers can spend less time waiting for results and more time applying them. Platforms using machine learning algorithms can help improve the pace of your workflow during clinical trials by delivering immediately through AI and machine learning technology.
What Does Machine Learning Have to Offer?
Machine learning can offer researchers more than just faster data delivery times. Platforms dedicated to the field of clinical trials and study have been designed to allow researchers to interface with partners around the world to expand their scope of knowledge more quickly and efficiently. This allows for the compiling of data on a much broader level, while also expediting the construction of data models.
When it comes to discovering a new cure or treatment, the clinical trial result can sometimes take months to materialize. The healthcare research field relies on fast delivery times, and the synthetic data offered through machine learning can make taking steps toward new cures and treatments that much faster.
Instead of waiting months for trail deployment and the facilitation of targeted patient recruitment, machine learning platforms can tackle these issues in a matter of hours. The original Clinical data is transformed into privacy-compliant synthetic copies without exposing sensitive information.
What is Synthetic Clinical Data?
Synthetic health data is statistically similar to patients’ health data regarding information about their medical conditions, such as diabetes, hypertension, blood pressure, and weight. However, there is no personally identifiable information, such as names, contact information, or social security numbers.
Unlike de-identification, which hides identities while using actual patient data, the De-identification process is not foolproof, and after stripping data of personally identifiable information, the remaining data may be of limited utility. This is the advantage of synthetic data. The dataset contains patient identifying information (ID) and patient-level lab measurement data. The dataset contains statistical near-identical information patient data, but no identifiable information of the patent.
The algorithms that build the synthetic data are complex; however, the concept is simple. For example, consider an original health care dataset of 100,000 blood pressure readings, each attached to an actual patient. The computer algorithm utilizes machine learning capabilities and observes the trends of the original data set to convert this real-world data into synthetic data, Then, it will observe average and trends in blood pressure (such as associated age, gender, and how blood pressure changes).
These algorithms will be used to generate synthetic data based on the statistical characteristics of the actual data. The result is a data set of blood pressure readings that are not attached to any actual patient or patient identifiable information but are clinically accurate and valuable. Notably, the synthetic data cannot be traced back to the original patients, unlike some de-identified or anonymous data, which have been vulnerable to re-identification attacks.
Why Synthetic Data is Better?
The goal of synthetic data is to provide a clinical pathway for cures instead of treatment. While it can also help fast-track new treatments, its focus is to facilitate advancements in finding cures for major diseases, new and old.
By operating on a fast and agile analytics platform, machine learning can help you deploy quickly, while also being able to create data in hours instead of months. Tests can also be run with virtual datasets, eliminating the need for costly and lengthy testing. This evolving technology also uses differential privacy to protect the personal information of clinical trial participants in a more efficient manner than more traditional methods.
Additionally, automated processes with machine learning can cut costs by reducing the time needed to complete trials and run data analytics. AI-powered analytics can dive deeper into fragmented sources to extract more meaningful insights that may have otherwise been hidden in the data.
Synthetic data is also more accurate. Machine learning platforms can guarantee accuracy in all facets of clinical research, including testing, patient safety, and product quality. Accurate data can then be seamlessly extended to collaborate with multiple teams and even external research institutions.