Neglected Tropical Diseases (NTDs) are a disparate group of conditions which afflict over a billion people worldwide, largely in impoverished, tropical areas. Despite the significant mortality, morbidity, and the economic impact associated with these diseases, NTDs are often overlooked in the global health policy agenda. This means that clinical trials for NTDs are of incredible importance.
However, running clinical trials is an inherently risky venture with a high failure rate. Unsurprisingly, NTD clinical trials are also fraught with the risk of failure. These trials often involve a high degree of complexity due to the environmental conditions, the presence of animal vectors, and complex life cycles associated with NTDs.
With the stakes being so high, being able to estimate the risk of failure of NTD clinical trials from the outset can be invaluable in planning and managing these trials effectively.
This is where the Clinical Trial Risk Tool, developed by Fast Data Science, comes into play. This sophisticated tool leverages machine learning to predict the risk associated with a given trial by analysing the trial’s protocol text.
Every trial begins with a protocol - a document that describes the objectives, study design, methodology, statistical considerations, and other details. This tool interprets this text, highlighting any potential risks in the trial design, analysis plan, participant demographics, and other variables that could compromise the trial’s informativeness.
Fast Data Science trains their machine learning models on a vast and diverse array of protocols and outcomes from previous trials. The models learn which factors contribute to a trial’s success or failure. When presented with a new protocol, the tool makes an informed prediction about the risk of the trial failing to end informatively. By using retrospective trial data, the tool can also identify the level of risk based on past trials in similar environments or disease areas.
Crucially, this tool allows researchers to assess and address any risks before the trial gets underway, thereby minimizing the chance of an uninformative or unsuccessful trial. This can save countless resources and, ultimately, lives, in the battle against NTDs.
In conclusion, being able to predict the risks associated with NTD clinical trials can greatly improve their success rates. Fast Data Science’s Clinical Trial Risk Tool provides a cutting-edge, data-driven solution to this challenge, paving the way for more effective and efficient progress in our fight against these devastating diseases.
