Coming out soon
We have developed a machine learning and rule based tool called the Clinical Trial Risk Tool using natural language processing. The Clinical Trial Risk Tool allows a user to upload a trial protocol and which categorises the protocol as high, medium, or low risk of ending uninformatively.
When a pharmaceutical company develops a drug, it needs to pass through several phases of clinical trials before it can be approved by regulators.
Before the trial is run, the drug developer writes a document called a protocol. This contains key information about how long the trial will run for, what is the risk to participants, what kind of treatment is being investigated, etc.
The tool is open-source under MIT licence and it does not save any of your data.
Currently, professionals at a funding organisation read the protocols and perform a subjective assessment of the trial’s cost, complexity, and risk of ending uninformatively.
One of the most common causes of a trial ending uninformatively is underpowering. 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. Low-risk trials are often run by well-known institutions with external funding and an international or intercontinental array of sites. These indicators can be referred to as features or parameters.
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}
}Blog
On 8 October, Thomas Wood of Fast Data Science presented the Clinical Trial Risk Tool, along with the Harmony project, at the AI and Deep Learning for Enterprise (AI|DL) meetup sponsored by Daemon. You can now watch the recording of the live stream on AI|DL’s YouTube channel below: The Clinical Trial Risk Tool leverages natural language processing to identify risk factors in clinical trial protocols. The initial prototype Clinical Trial Risk Tool is online at https://app.
Shining a Light on Clinical Trial Risk: Exploring Clinical Trial Protocol Analysis Software Clinical trials are the backbone of medical progress, but navigating their design and execution can be complex. Fast Data Science is dedicated to helping researchers by analysing clinical trial protocols through the power of Natural Language Processing (NLP). We are presenting a selection of software which can be used for clinical trial protocol analysis or clinical trial cost prediction and risk assessment.
Understand your clinical trials Are your clinical trials risky? Are costs running away? It’s very tricky to estimate clinical trial costs before a trial is run. Try our free cost calculator. This is a regression model, trained on real clinical trial data. Trial is for condition HIV Tuberculosis COVID Influenza Malaria Enteric and diarrheal diseases Neglected tropical diseases Polio Diabetes Pneumonia Hypertension (see full product) Motor neurone disease (see full product) Multiple sclerosis (see full product) Obesity (see full product) Sickle cell anemia (see full product) Stroke (see full product) Cystic fibrosis (see full product) Cancer (see full product) Other (see full product) Phase Early Phase 1 1 1.