Phastar is a global, data-focused contract research organisation (CRO). They work with small and large biotechnology, pharmaceutical, and medical companies to provide statistical consulting, analysis, and reporting, clinical data management, and data science services.
Last year, we partnered with Phastar on a collaborative project focused on automating the medical coding process for adverse events using advanced machine-learning techniques.
In clinical trials, adverse events are coded using the MedDRA coding dictionary to standardize and allow consistent interpretation of results. There are five MedDRA classifications that each verbatim term (the term reported during the trial) needs to be mapped to. Even with the aid of auto-coders, this is a manual and time-consuming process that is prone to human error. The recent wider adoption of machine learning within clinical trials has led to the semi-automation of certain tasks to increase efficiency in the clinical trial process.
The focus of this project was the verbatim mapping to the lowest level term (LLT) in the MedDRA hierarchy. Our goal was to ascertain if the auto-coding process could be improved with the application of Natural Language Processing (NLP) to the verbatim terms, which would suggest a list of the most appropriate LLT for the verbatim term with a confidence interval for adjudication by the data manager.
To delve deeper into this project and learn more about the outcomes from our collaboration with Phastar, we invite you to read the full blog post on their website: https://phastar.com/resources/blog/302-data-science-collaboration-with-ishango-ai-2