Upcoming Dash in Action webinar on 7 June!

· Thomas Wood
Upcoming Dash in Action webinar on 7 June!

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On 7 June, 2023, at 6pm UK time (1pm EDT), we will be presenting in the webinar Dash in Action: Image Processing, Forecasting, NLP. This is a community showcase hosted by Plotly, a company in Canada which makes the graphing software Plotly.

Sign up for the webinar at https://go.plotly.com/dash-in-action!

The speakers come from a variety of backgrounds, organisations and industries, all over the world:

NLP visualisation

Explainable NLP

The best language models combined with brilliant data visualisation.

We will be presenting the following apps:

🥇 Clinical Trial Risk Dash App by Thomas Wood

Try it at: https://app.clinicaltrialrisk.org/.

Thomas Wood has developed a dashboard funded by the Bill and Melinda Gates Foundation to analyse the risk of a clinical trial failing. The Foundation had a number of incoming clinical trials and they had a need to identify risk factors and triage the trials. When someone runs a clinical trial, they write a 200-page document which is called a protocol, which describes how the trial will be run, where it takes place, how many participants are needed, and how the data collected will be analyzed. Thomas built some natural language processing models which can identify risk factors in the text, and developed a front end in Dash allowing users to run the models and understand the decision making process.

  1. 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).

🥈 SARIMA Tuner by Gabriele Albini

Try it at: https://gabria1.pythonanywhere.com/.

Gabriele Albini has developed a dashboard which allows users to experiment with sARIMA models (seasonal autoregressive integrated moving average). sARIMA models are models which allow us to predict time series with seasonality. For example, you may use a sARIMA model to predict daily electricity loads for a building or city, or to predict customer spend for a large supermarket.

Screenshot of the sARIMA Tuner app

🥉 Product Environmental Report Dash App by Matteo Trachsel

Try it at: https://report.thermoplan.ch/.

Matteo Trachsel works for the environmental consultancy Thermoplan and has developed a dashboard which calculates the carbon footprint of coffee machine usage.

Screenshot of the Thermoplan app

🏅 MRI image processing app by Agah Karakuzu

Try it at: https://rrsg2020.db.neurolibre.org/.

Agah Karakuzu at Polytechnique Montréal has built the RRSG 2020 Dashboard to accompany a scientific article on MRI. Neuroscientists are interested in a value called T1, which is the time it takes water molecules in the brain to return to their original state following a magnetic pulse. The purpose of the study was to assess the reproducibility of T1 values across different sites and vendors where researchers used the same research protocol. The dashboard allows researchers to compare datasets from brains and phantoms (calibration devices containing water, used to test MRI machines) from the three main MRI machine vendors (Phillips, GE and Siemens). This will be demonstrated by his colleague Nadia Blostein.

Screenshot of the MRI image processing app app

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}
}

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