The landscape of clinical trials has witnessed significant evolution over the years with an increase in complexity being a critical dimension. This development has direct implications on the trial timelines, success rates, and other pivotal factors. Based on a comprehensive analysis of over 16,000 trial protocols by Markey et al. in their ground breaking paper, “Clinical trials are becoming more complex: a machine learning analysis of data from over 16,000 trials”, it is evident that the complexity of clinical trials has been on the rise across various clinical phases and therapeutic areas.
In between various therapeutic areas, Malaria clinical trials have also witnessed increase in complexity over time. Malaria, being a disease which primarily impacts impoverished regions, often faces challenges in terms of trial recruitment, adherence to treatment and follow up, further adding to the complexity of the trials.
In this dynamic environment, tools that can predict the complexity of a trial can be extremely beneficial for sponsors and other stakeholders. One such tool is the Clinical Trial Risk Tool developed by Fast Data Science. This tool leverages machine learning algorithms to accurately predict the complexity of a Malaria clinical trial based on the protocol text.
Using concepts from natural language processing (NLP), the Clinical Trial Risk Tool can effectively extract and quantify important featuress from the protocol text such as, key demographics, trial design properties, number of endpoints, inclusion/exclusion criteria and other relevant features, combining them into a Trial Complexity Score which correlates with the overall complexity of the clinical trial.
Importantly, machine learning algorithms are wired to learn from past data and improve over time. Hence, the predictions made by Fast Data Science’s Clinical Trial Risk Tool show promise over time as more and more data gets incorporated into the machine learning model.
In a domain where precision is vital, the promise of predicting the complexity of a Malaria clinical trial from its protocol document can greatly enhance clinical trial planning and execution. It paves way for precision medicine, early risk management and better allocation of resources.
Indeed, the effective use of machine learning in deciphering the complexity of clinical trials is pushing the boundaries of modern medical research and has the potential to disrupt traditional ways to conduct clinical trials.
