We have developed a machine learning and rule-based tool using natural language processing which allows a user to upload a trial protocol, and which categorises the protocol as high, medium or low risk of ending uninformatively. The tool is at https://clinical.fastdatascience.com and is open-sourced on Github. You can read an explanation of how the tool works here, and a description of how we validated its accuracy here.
There are several indicators of high risk of uninformativeness which can be identified in a protocol, such as a lack of and or an inadequate statistical analysis plan, use of non-standard endpoints, or the use of cluster randomisation. One of the most common causes of a trial ending uninformatively is underpowering. Low-risk trials are often run by well-known institutions with external funding and an international or intercontinental array of sites. These indicators can be referred to as features or parameters.
This project is an initial Proof of Concept (POC) which to showcase what is possible with natural language processing, with a view to moving towards a more comprehensive main project which may identify a more complete set of cost, complexity, or uninformativeness risk factors.
The tool is designed with a feedback form so that inaccurate data extractions can be reported back to the developers.
In addition the MIT License means that you are free to add features or extend the scope of the tool.
We hope that researchers who are considering submitting a protocol of a trial to a prospective source of funding will be able to use the tool as a kind of checklist to ensure that their trial is designed to reduce risk and increase the prospects of being funded.
This post originally appeared on Fast Data Science’s blog on LinkedIn. Clinical trials are essential for medical advancement but are not without risk. Delays, budget overruns, and compliance issues can derail the most carefully planned studies. Proactive risk management is the key to ensuring patient safety, maintaining regulatory compliance, and achieving successful trial outcomes. In this article we’ll explore the key risks in clinical trials, how AI-powered tools like the Clinical Trial Risk Tool can help mitigate these risks, and practical strategies for ongoing risk monitoring.
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