The drive to develop effective and universally-applicable influenza vaccines has encouraged many organisations to conduct clinical trials. These trials, however, are multifaceted endeavors riddled with nuanced complexities and challenges. One of the biggest issues at play is the risk of failure – or more specifically, the risk of a trial ending un-informatively.
Clinical trials that end this way don’t provide valuable insights to guide clinical, policy, or research decisions. This is concerning, as research by Hutchinson et al delivered alarming statistics – only 26.4% of the 125 clinical trials they studied met the conditions for informativeness. This unfortunate statistic reveals the need to estimate and manage the risk of failure in influenza clinical trials better.
Enter Fast Data Science’s Clinical Trial Risk Tool.
Fast Data Science’s Clinical Trial Risk Tool is designed to harness the dynamic power of machine learning to predict the risk of failure in influenza clinical trials from the text of their protocols. This groundbreaking approach aims to improve the management and implementation of these trials, ultimately reducing the risk of ending in an uninformative manner.
Machine learning utilises powerful algorithms that can learn from and leverage historical data. By feeding the tool with a large amount of past trials – both successful and unsuccessful – it can identify patterns and correlations that could indicate a higher risk of failure. Specific algorithms can then classify a new trial’s protocol based on its characteristics, predicting potential outcomes.
Key factors such as the number of participants, the length of the trial, the number of uncontrolled variables, and the ability to control exposure contribute heavily to a trial’s failure or success. These and many more factors are what the machine learning models of Fast Data Science’s Risk Tool consider when making predictions.
For instance, the tool can flag a trial as high risk if its protocol is missing a completed Statistical Analysis Plan (SAP), a common marker indicating a lack of thoroughness and ultimately a higher potential for failure.
Fast Data Science’s Clinical Trial Risk Tool is a significant step forward in our ability to manage influenza clinical trials. Through leveraging machine learning, we can better predict and manage the risk of uninformative outcomes, thereby enhancing the efficiency and success rate of these critical trials. Protecting society from the devastating impacts of influenza outbreaks necessitates such innovation - and machine learning is at the forefront of this pivotal endeavour.
