We’re proud to announce that the Clinical Trial Risk Tool has been selected as a winner of the Plotly Dash Example Apps Challenge (2023), out of 25 amazing apps submitted by the community!
You can try the tool at clinicaltrialrisk.org.
Natural language processing
Funded by the Bill and Melinda Gates Foundation, this app uses natural language processing, Plotly Dash, and the spaCy and Scikit-Learn libraries to calculate the risk of a clinical trial failing to deliver informative results. It reads the trial protocol and identifies key features from the text which are fed into a risk model.
Thomas Wood will be presenting the tool in a webinar on 7 June 2023 which you can book here.
Thank you to all #PlotlyCommunity members who participated in the recent #Dash Example Apps Challenge, and congratulations to the winning submissions!
— Plotly (@plotlygraphs) May 22, 2023
🥇 Clinical Trial Risk Dash App by Thomas Wood
🥈 SARIMA Tuner by Gabriele Albini
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Meanwhile, an article describing the tool has been published at: Wood TA and McNair D. Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness. Gates Open Res 2023, 7:56 (https://doi.org/10.12688/gatesopenres.14416.1).
Download the pitch deck for the Clinical Trial Risk Tool
If you would like to cite the tool alone, you can cite:
Wood TA and McNair D. Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness. Gates Open Res 2023, 7:56 doi: 10.12688/gatesopenres.14416.1.
A BibTeX entry for LaTeX users is
@article{Wood_2023,
doi = {10.12688/gatesopenres.14416.1},
url = {https://doi.org/10.12688%2Fgatesopenres.14416.1},
year = 2023,
month = {apr},
publisher = {F1000 Research Ltd},
volume = {7},
pages = {56},
author = {Thomas A Wood and Douglas McNair},
title = {Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness},
journal = {Gates Open Research}
}
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Many companies and organisations have large datasets that are stored in a very unstructured format. For example, you could work for a US based healthcare provider or insurer and have patient records stored in a free text format such as HL7 files or PDFs. A building regulator, land registry, or mortgage provider may have texts and accompanying diagrams from thousands of building inspections or land title deeds. A patent attorney’s office may have records of patent applications in PDF format.
On 20 May, I attended the Expert Witness Conference in Dublin, Ireland, organised by La Touche Training. It was an eye opening event with a mixture of lawyers and expert witnesses in different fields from Ireland and abroad. The event was chaired by Mr Justice Michael Peart, with a keynote address by the Honourable Mr Justice David Barniville, President of the High Court of Ireland.
Fast Data Science at Ireland’s Expert Witness Conference on 20 May 2026 in Dublin Links to guidance on legal AI issued by legal authorities and other organisations Official guidance UK: Artificial Intelligence (AI) Guidance for Judicial Office Holders, 31 October 2025. https://www.judiciary.uk/wp-content/uploads/2025/10/Artificial-Intelligence-AI-Guidance-for-Judicial-Office-Holders-2.pdf
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