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.
The tool which can estimate the risk of HIV and TB trials ending uninformatively and will soon be extended to cover other metrics such as trial complexity and cost.
The NLP model was developed as an ensemble of components which extracted different aspects of information from the text, including rule-based (hand-coded) and neural network designs.
The model’s output features were then condensed down via a clinical trials risk model which ultimately produces a three-level risk traffic light score. The full analysis can be exported as XLSX or PDF.
This post originally appeared on Fast Data Science’s blog on LinkedIn. Clinical trials are essential for medical advancement but are not without risk. Delays, budget overruns, and compliance issues can derail the most carefully planned studies. Proactive risk management is the key to ensuring patient safety, maintaining regulatory compliance, and achieving successful trial outcomes. In this article we’ll explore the key risks in clinical trials, how AI-powered tools like the Clinical Trial Risk Tool can help mitigate these risks, and practical strategies for ongoing risk monitoring.
This post originally appeared on Fast Data Science’s blog on LinkedIn. Discover how the Clinical Trial Risk Tool helps optimise clinical trial workflows with accurate risk and cost analysis. Save time and reduce costs. Why Workflow Efficiency Matters in Clinical Trials Running a clinical trial is a complex and expensive process. Delays, unexpected costs, and inefficiencies can waste time and money, affecting trial outcomes and patient care. As trials become more complicated, workflow management is more important than ever.
This post originally appeared on Fast Data Science’s blog on LinkedIn. In today’s ever-evolving healthcare landscape, technology is crucial to improving patient care, streamlining processes, and enhancing outcomes. Natural Language Processing (NLP) is one such technology that is revolutionising the way healthcare organisations operate. For instance, NLP has been used to analyse patient feedback and identify trends in satisfaction levels, leading to targeted improvements in service quality. In this article, we will explore the role of NLP in healthcare, its benefits, and potential applications.
