In March 2019, Health Data Insight joined forces with Professor Mihaela van der Schaar and eight data science PhD students from the Machine Learning and Artificial Intelligence for Medicine group to explore how machine learning can be used to improve outcomes in...read more
What is the opportunity? We are offering up to five internships working on data collected by the largest cancer registry in the world. You will have the opportunity to...read more
This new database revolutionises the way scientists and researchers investigating cancer analyse the disease. The Simulacrum, so called because it artificially simulates real data about cancer without any risk of individual patients being identified, is a breakthrough...read more
In November 2018, HDI held its annual symposium. Once again the teams excelled themselves by showing the projects they’ve been working on. This years’ hot topics included looking at ways to use primary care prescription data to find people who might be suspected of...read more
The interns who came to spend the summer with HDI this year found their time enjoyable and worthwhile, as you can see from the video below: https://vimeo.com/235156938read more
HDI has been awarded a Cancer Research UK Pioneer Award for research to improve the early diagnosis of cancer using machine learning and computer-based inference algorithms. We will analyse very large linked and unlinked datasets to ask:
1) Is it possible to identify patterns in medication given prior to the diagnosis of cancer and other data to derive an “index of suspicion” that a patient is at increased likelihood of developing subsequent cancer?
for different types of cancer; our suspicion is that the index may be most valuable for patients with cancers that present with vague symptoms – e.g. pancreatic, ovarian, stomach or brain cancer.
for different stages of presentation of the same cancer: for example, can we use the index to help identify common cancers at an earlier stage when they would have a better prognosis?
2) Using the index of suspicion derived in 1), can we risk-stratify patients in the unlinked prescription data to identify those who might be most at risk of developing a particular cancer?