Natural language processing is becoming ever more important in research. The explosion in interest in LLMs is sparking many opportunities to analyse text data in healthcare, social sciences, and other areas. 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. 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.
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 Dash users community! The trial risk app is built on Plotly Dash, a front end graphical software package. Thomas Wood from Fast Data Science 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!
We have developed a machine learning and rule-based tool using natural language processing which allows a user to upload a trial protocol, and which categorises the protocol as high, medium or low risk of ending uninformatively. The tool is at https://clinicaltrialrisk.org/tool and is open-sourced on Github. You can read an explanation of how the tool works here, and a description of how we validated its accuracy here. Situation There are several indicators of high risk of uninformativeness which can be identified in a protocol, such as a lack of and or an inadequate statistical analysis plan, use of non-standard endpoints, or the use of cluster randomisation.
We have developed a tool allowing researchers to analyse HIV and TB Clinical Trial Protocols and identify risk factors using Natural Language Processing. The tool allows a user to upload a clinical trial protocol in PDF format, and the tool will generate a risk assessment of the trial. You can find example protocols by searching on ClinicalTrials.gov. Details of this proof of concept The POC stage is limited to 2 pathologies: HIV and Tuberculosis (TB).
In order to develop the Clinical Trial Risk Tool, we had to conduct a quality control exercise on the components. Each parameter which is fed into the complexity model is trained and evaluated independently. For an overview of how the tool works, please read this blog post. Datasets I used two datasets to train and evaluate the tool: Manual dataset – this was a set of between 100 and 300 protocols which I read through individually and annotated key parameters such as the sample size.
I am pleased to announce the Clinical Trial Risk Tool, which is now open to the public to use. The tool is available at https://clinicaltrialrisk.org/tool. Screenshot of the tool The tool consists of a web interface where a user can upload a protocol in PDF or Word format, and ultimately a number of features were extracted, such as number of subjects, statistical analysis plan, effect size, number of countries, etc.
A technical paper on the Clinical Trial Risk Tool has been published in Gates Open Research! The Clinical Trial Risk Tool is a browser-based tool which uses Natural Language Processing (NLP) to analyse clinical trial protocols. We are pleased to announce the publication of a technical paper on the tool. Wood TA and McNair D. Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness [version 1; peer review: 1 approved with reservations].
