Clinical trials are the backbone of medical progress, but a worrying trend is emerging: a large portion end without delivering useful results. This “uninformativeness” wastes valuable resources and delays advancements.
Fast Data Science is excited to announce the publication of a technical research paper the Clinical Trial Risk Tool, a game-changer in identifying potential uninformativeness at the protocol stage!
The Clinical Trial Risk Tool is a browser-based tool which uses Natural Language Processing (NLP) to analyse clinical trial protocols. Here’s how it works:
Ready to fight uninformativeness? Head over to https://clinicaltrialrisk.org/tool and access this free, open-source software.
A BibTex citation is as follows:
@article{wood2023clinical,
title={Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness},
author={Wood, Thomas A and McNair, Douglas},
journal={Gates Open Research},
volume={7},
number={56},
pages={56},
year={2023},
publisher={F1000 Research Limited}
}
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Explore NLP JobsFast Data Science appeared at the Hamlyn Symposium event on “Healing Through Collaboration: Open-Source Software in Surgical, Biomedical and AI Technologies” Thomas Wood of Fast Data Science appeared in a panel at the Hamlyn Symposium workshop titled “Healing Through Collaboration: Open-Source Software in Surgical, Biomedical and AI Technologies”. This was at the Hamlyn Symposium on Medical Robotics on 27th June 2025 at the Royal Geographical Society in London.
We presented the Insolvency Bot at the 4th Annual Conference on the Intersection of Corporate Law and Technology at Nottingham Trent University Dr Eugenio Vaccari of Royal Holloway University and Thomas Wood of Fast Data Science presented “A Generative AI-Based Legal Advice Tool for Small Businesses in Distress” at the 4th Annual Conference on the Intersection of Corporate Law and Technology at Nottingham Trent University
What is generative AI consulting? We have been taking on data science engagements for a number of years. Our main focus has always been textual data, so we have an arsenal of traditional natural language processing techniques to tackle any problem a client could throw at us.
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