Neglected tropical diseases (NTDs) comprise a varied group of diseases that pose significant health, social and economic burdens. They affect over a billion people worldwide and yet, these diseases have historically been underrepresented on the global health policy stage. The design and implementation of effective clinical trials for NTDs therefore play a critical role in global health. It is invaluable for their eventual control and eradication.
One key measure in clinical trials is what we refer to as ‘informativeness’. Essentially, it represents how useful a trial is in guiding clinical, policy, or research decisions. Theoretically, an informative trial should have the capacity to shape our understanding of a disease, provide new insights into treatment avenues, or influence policy-making.
As you can imagine, judging the ‘informativeness’ of a trial is not a straightforward task. Factors like trial size, duration, and statistical analysis plan all play a key role in determining the informativeness of a study. Historically, this evaluation has been a manual process, but with the advent of machine learning, we are seeing a paradigm shift.
Fast Data Science’s Clinical Trial Risk Tool utilises machine learning algorithms to estimate the informativeness of a trial from its protocol text. This tool parses through a trial’s protocol, evaluating aspects such as the disease area, patient population, study design, proposed treatments, and the success of previous similar trials. By analysing these variables, the tool can predict, with a fair degree of accuracy, the likelihood of a study leading to meaningful and actionable results.
In the context of NTDs, a tool like this could be of immense value. By being able to predict the informativeness of a clinical trial in the early stages, it can aid in the allocation of often scarce resources towards studies that are likely to yield the most significant results. This could thereby expedite the journey towards developing effective intervention strategies for these diseases.
The Clinical Trial Risk Tool thereby serves as an example of how machine learning is reshaping the field of clinical research. As we gain further insights into these algorithms’ capabilities and continue to train them on more data, they could potentially become an indispensable tool in our arsenal against not just NTDs, but many other disease areas.
