Automated estimation of disease recurrence in head and neck cancer using routine healthcare data

Abstract

We have demonstrated that our algorithm can be used to automate the interpretation of routine datasets to extract survival information for this sample of patients. It currently underestimates recurrence rates due to many patients not being well-enough to be treated for recurrent disease. With some further optimisation, this technique could be extended to a national level, providing a new approach to measuring outcomes on a larger scale than is currently possible. This could have implications for healthcare provision and policy for a range of different disease types.