Clinical Trial Risk Tool wins Plotly Dash challenge!

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.

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!

🥇 Clinical Trial Risk Dash App by Thomas Wood
🥈 SARIMA Tuner by Gabriele Albini
🥉 Product Environmental Report Dash App by…

— Plotly (@plotlygraphs) May 22, 2023

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

How to cite 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}
}