Diarrheal infections are inadequately addressed killers in low-income countries, causing more than 500,000 mortality cases in children every year. Equally pernicious is enteric fever, bringing about 25,000 deaths annually. These diseases, contracted primarily due to poor sanitation and contaminated water, contribute to severe health impacts like stunting and impaired cognitive development.
Vaccines have proven to be a cost-effective means of protection against such pathogens. Yet, the success rate of clinical trials necessary for these vaccines is a significant concern. A recent study by Hutchinson et al (2022) revealed that only 26.4% of the clinical trials conducted across ischemic heart disease, diabetes mellitus, and lung cancer resulted in an informatics change in clinical, research, or policy decisions. This startling reality begs the question: How do we improve the informativeness of our trials?
Here comes Fast Data Science’s Clinical Trial Risk Tool, which leverages machine learning to estimate the risk of failure in enteric and diarrheal diseases clinical trials.
The tool facilitates risk prediction by analysing the trial protocol text, which typically includes the study design, methodology, duration, and possible uncontrolled variables. With AI technology, it can predict risk factors such as the absence of a complete Statistical Analysis Plan (SAP), a critical parameter often associated with higher trial risk.
In essence, Fast Data Science’s tool extracts variables from trial protocols, concurrently learning from past trials that resulted in failure or success. It then applies this knowledge to predict the possible outcome of a new trial based on its protocol, thereby providing critical insights early in the trial design phase.
It is worth noting the importance of such risk assessment tools for funders, researchers, and policy-makers. Predicting a trial’s likely informativeness can guide strategic decisions, enabling efficient allocation of resources, enhancing the development of safe and effective vaccines, and ultimately advancing the fight against enteric and diarrheal diseases.
Marrying healthcare with artificial intelligence, the Fast Data Science’s Clinical Trial Risk Tool is an essential instrument for augmenting the success of clinical trials, and understanding the links between protocol text and trial outcome. This leading-edge approach could very much be the key to unlocking the constraints of vaccine development, and turning the tide against deadly diseases.
