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.
Clinical trial designs vary considerably, impacting study execution, patient recruitment, endpoints, and treatment delivery. Here’s a brief summary of some common designs: First-In-Human (FIH) Studies These are the initial human trials for a new drug, procedure, or treatment, focusing primarily on safety. Cohort Studies These observational studies follow a group of individuals over an extended period to assess risk factors associated with developing specific conditions. Case-Control Studies These studies compare individuals with a particular disease or condition (cases) to similar individuals without the disease (controls) to identify potential risk factors.
This post originally appeared on Fast Data Science’s blog on LinkedIn. The Growing Role of AI in Clinical Trials Clinical trials are vital for advancing medicine, but managing them efficiently is a constant challenge. Traditional methods for assessing risks and estimating costs often miss the mark, leading to delays and unexpected expenses. This is where Artificial Intelligence (AI) and Natural Language Processing (NLP) come into play, offering smarter, data-driven solutions to streamline trial planning and management.
This post originally appeared on Fast Data Science’s blog on LinkedIn. Budgeting is one of the most critical steps when planning a clinical trial. Clinical trials are complex, multi-phase studies that require significant resources, and understanding the costs associated with each phase is crucial for an accurate clinical trial budget. In this post, we’ll explore the different phases of clinical trials and the key factors that influence their costs, providing insights into how to prepare a comprehensive budget that aligns with your trial’s needs.
