This post originally appeared on Fast Data Science’s blog on LinkedIn. The Growing Role of AI in Clinical Trials Clinical trials are vital for advancing medicine, but managing them efficiently is a constant challenge. Traditional methods for assessing risks and estimating costs often miss the mark, leading to delays and unexpected expenses. This is where Artificial Intelligence (AI) and Natural Language Processing (NLP) come into play, offering smarter, data-driven solutions to streamline trial planning and management.
This post originally appeared on Fast Data Science’s blog on LinkedIn. Budgeting is one of the most critical steps when planning a clinical trial. Clinical trials are complex, multi-phase studies that require significant resources, and understanding the costs associated with each phase is crucial for an accurate clinical trial budget. In this post, we’ll explore the different phases of clinical trials and the key factors that influence their costs, providing insights into how to prepare a comprehensive budget that aligns with your trial’s needs.
This post originally appeared on Fast Data Science’s blog on LinkedIn. Budgeting is one of the most critical steps when planning a clinical trial. Clinical trials are complex, multi-phase studies that require significant resources, and understanding the costs associated with each phase is crucial for an accurate clinical trial budget. In this post, we’ll explore the different phases of clinical trials and the key factors that influence their costs, providing insights into how to prepare a comprehensive budget that aligns with your trial’s needs.
The Clinical Trial Risk Tool has been featured in a guest column in Clinical Leader, titled A Tool To Tackle The Risk Of Uninformative Trials, in cooperation with Abby Proch, Executive Editor at Clinical Leader. In the article, Thomas Wood of Fast Data Science highlights the problem of “uninformative” clinical trials – those that don’t provide meaningful results, even if the drug being tested is effective or ineffective. He distinguishes these from simply “failed” trials and emphasises the ethical and financial waste they represent.
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 Clinical Trial Risk Tool is online at https://clinical.
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.
This month, I presented the Clinical Trial Risk Tool at the Plotly Dash in Action webinar. I was interviewed by Plotly’s Community Manager Adam Schroeder. You can watch the relevant part of the webinar below. The Clinical Trial Risk Tool was one of four interactive apps presented as part of the webinar. The speakers at the webinar were: Matteo Trachsel, Head of Sustainability at Thermoplan AG, Switzerland, who presented his Product Environmental Report Dash App, which calculates the carbon footprint of coffee machine usage.
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://clinical.fastdatascience.com 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 please to announce the Clinical Trial Risk Tool, which is now open to the public to use. The tool is available at https://clinical.fastdatascience.com. 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].
