Quantum computing has the potential to dramatically change many aspects of healthcare analysis. For our interests this mostly features around increased efficiency in optimisation and hybrid ML-Quantum approaches. This article briefly describes our use-cases submissions to the annual NQCC Hackathon to showcase the emerging interests for healthcare using this technology.
Welcome to the first installment of our “Interview with a Data Scientist” series, where we explore the careers and work of the talented members of the NHS England Data Science team. We aim to showcase the fantastic individuals who contribute to the NHS England Data Science Profession and provide valuable insights for those considering a career in Data Science within the healthcare sector.
The first interview is with Joe Wilson, a former Engineer whose desire to make the perfect F1 Fantasy Team during the COVID pandemic drove him to become a Data Scientist who won’t stop talking about Reproducible Analytical Pipelines.
This interview orginally was published in the March edition of the Data Science Community for Health and Care Newsletter. You can subscribe and join the community here.
📢 Our spotlight today will be on Confident Presenting!
Do you want to present as speakers like Steve Jobs, Ken Robinson, or Simon Sinek, who are known for their exceptional ability to captivate and engage their audience? Keep reading to discover the techniques they use to deliver impactful and memorable presentations, building on the techniques from our away day workshop. Also make sure to check out some of our other resources for presenting!
We have built a proof-of-concept tool which will help assurers, data scientists and clinicians to evaluate AI classifiers. We call this the RISE tool, it utilises LLM's, AI Image Generators and an interactive plot to allow users to easily evaluate image classifiers. We carried out careful experimentation to ensure its effectiveness, and plan to continue this research in the future.
The NHS England Data Science team, as well as a range of other analysts from across the organisation, attended an AI Hackathon at Microsoft, organised by the Data Science Team together with Microsoft and Kainos, with the key stakeholders being the NHS Websites Services Team. In this article, the author shares her experiences at the event.
We have been building a proof-of-concept tool that scores the privacy risk of free text healthcare data. To use our tool effectivly, users need a basic understanding of the entities within their dataset which may contribute to privacy risk.
There are various tools for annotating and exploring free text data. The author explores some of these tools and discusses his experiences.
Over recent years, larger, more data-intensive Language Models (LMs) with greatly enhanced performance have been developed. The enhanced functionality has driven widespread interest in adoption of LMs in Healthcare, owing to the large amounts of unstructured text data generated within healthcare pathways.
However, with this heightened interest, it becomes critical to comprehend the inherent privacy risks associated with these LMs, given the sensitive nature of Healthcare data. This PhD Internship project sought to understand more about the Privacy-Risk Landscape for healthcare LMs through a literature review and exploration of some technical applications.
Reproducible analytical pipelines (RAP) help ensure all published statistics meet the highest standards of transparency and reproducibility. Sam Hollings and Alistair Bullward share their insights on adopting RAP and give advice to those starting out.
Reproducible analytical pipelines (RAP) are automated statistical and analytical processes that apply to data analysis. It’s a key part of national strategy and widely used in the civil service.
Over the past year, we’ve been going through a change programme and adopting RAP in our Data Services directorate. We’re still in the early stages of our journey, but already we’ve accomplished a lot and had some hard-learnt lessons.