Kidney failure, leishmaniasis, and schistosomiasis are just a few examples of Neglected Tropical Diseases (NTDs). Due to their prevalence in impoverished populations, clinical trials for these conditions often present unique challenges that can increase their overall complexity. For stakeholders planning such trials, the ability to predict perceived complexities could save valuable resources and potentially accelerate needed treatments to market.
Recently, a team of researchers highlighted the increasing complexity in clinical trials, including those related to NTDs. By analyzing over 16,000 trials using machine learning, the group developed an innovative metric known as the Trial Complexity Score (TCS). This measure, combined with regression analysis, enabled them to predict overall clinical trial duration based on various features like the number of endpoints and inclusion-exclusion criteria.
With these advancements, Fast Data Science presents its Clinical Trial Risk Tool, a revolutionary solution for predicting trial complexity in NTDs from the protocol text.
Building upon previous research, Fast Data Science taps into the potential of machine learning to gauge trial complexity. To start with, the study protocol is converted from text to a numeric representation. This process involves tokenization, where the text is broken down into individual words, or “tokens”, and each word is assigned a corresponding number.
Once the protocol text is prepared, the machine learning algorithm takes over. What it does is map the text to the TCS by training on previous trial data. This essentially means it identifies patterns from past trials and applies those learnings to estimate the TCS of a new trial.
Predicting the complexity of NTD clinical trials goes beyond gauging the level of procedural intricacy. The Fast Data Science’s Clinical Trial Risk Tool benefits users in numerous ways.
Firstly, anticipating complexity before trial commencement allows more accurate cost and timeline projecting. Secondly, identifying potential roadblocks or complicated procedures can lead to better risk management strategies. This proactive approach can boost efficiency and effectiveness, leading to safer, more streamlined trials, and ultimately paving the way to faster access to essential treatments in vulnerable communities.
Advancements like the Clinical Trial Risk Tool offer a promising future for the clinical trial industry. Employing machine learning for trial complexity estimation can lead to better risk management, greater operation efficiency, and accelerated delivery of important therapies to patients suffering from NTDs. Thus, this approach embodies the power of data science in transforming health care systems, one clinical trial at a time.
