Employee

BIG DATA AND ARTIFICIAL INTELLIGENCE

Patient-level data to understand patients' care

We have a long history of using large national datasets to understand and improve clinical care. In particular, we have experience in longitudinal data analysis along clinical pathways and across multiple treatment pathways. We lead the GlioCova project which gives a national view of brain tumour care, variation, costs and outcomes.

We have developed a range of imaging-based AI tools to help analyse clinical data at scale. Our focus is on useful, robust applications of AI in clinical care. Recent examples include our development of an end-to-end pipeline to determine muscle mass in brain tumour patients, and showing that this predicts patient survival, and scalable approaches to clustering and predicting outcomes in patients.

We are now applying this to using AI to improve clinical care pathways.

To learn more about the Gliocova project, visit the Imperial College blog by clicking the button below:

 

PUBLISHED WORK

GLIOCOVA: TREATMENT AND HOSPITAL ADMISSIONS FOR PATIENTS WITH GBM IN ENGLAND

October 2021

Radvile Mauricaite, Kerlann Le Calvez, Matthew Williams

P14.27 EXPLORING END-OF-LIFE CARE IN THE GLIOCOVA NATIONAL BRAIN TUMOUR PATIENT COHORT

September 2021

R Mauricaite, K Le Calvez, J Droney, M Caldano, M Alam, M Williams

OS14.6.A GLIOCOVA: DEFINING PATIENT SAFETY EVENTS FOR BRAIN TUMOUR PATIENTS UNDERGOING NEUROSURGERY

September 2021

R Mauricaite, K Le Calvez, A Brodbelt, A Bottle, M Williams

P14.18 PATIENT AND PUBLIC INVOLVEMENT TO DEFINE PATIENT-CENTRED OUTCOMES FROM NATIONAL CANCER DATASETS

September 2021

L Pakzad-Shahabi, C Cherrington, N Brassil, P Even, D Gardner, W Fulcher, K Le Calvez, R Mauricaite, M Williams