Developing a risk score for pancreatic cancer diagnosis

Using machine learning techniques applied to linked routine data: a pilot study

This project on pancreatic cancer initiated in 2019, with an award from the Pancreatic Cancer Research Fund.  The aim is to use historic GP and hospital data to identify patients who are most likely to have early stage pancreatic cancer. We are using machine learning techniques to understand if there are combinations of particular health problems, illnesses, or symptoms experienced only by patients who are later diagnosed. Ultimately, improving the triage of these patients means that targeted diagnostic tests could lead to the pancreatic cancer being diagnosed earlier and treated more effectively.

ESMO WCGIC 2020 | Machine learning techniques in pancreatic cancer screening | VJOncology

Machine learning techniques in pancreatic cancer screening presented at 2020 ESMO WCGIC and published in Annals of Oncology | ESMO World GI press release

For an ‘In Focus’ Blog Post published online by AJMC please click here.

Project Staff

Dr. Laura Woods

Principal Investigator
Associate Professor in Epidemiology

Dr. Ananya Malhotra

Research Fellow