2 code implementations • 13 Dec 2022 • Zeljko Kraljevic, Dan Bean, Anthony Shek, Rebecca Bendayan, Harry Hemingway, Joshua Au Yeung, Alexander Deng, Alfie Baston, Jack Ross, Esther Idowu, James T Teo, Richard J Dobson
We explore how temporal modelling of patients from free text and structured data, using deep generative transformers can be used to forecast a wide range of future disorders, substances, procedures or findings.
no code implementations • 21 Dec 2018 • Sebastian Vollmer, Bilal A. Mateen, Gergo Bohner, Franz J. Király, Rayid Ghani, Pall Jonsson, Sarah Cumbers, Adrian Jonas, Katherine S. L. McAllister, Puja Myles, David Granger, Mark Birse, Richard Branson, Karel GM Moons, Gary S Collins, John P. A. Ioannidis, Chris Holmes, Harry Hemingway
Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing sources of data for patient benefit.
no code implementations • 23 Nov 2018 • Spiros Denaxas, Pontus Stenetorp, Sebastian Riedel, Maria Pikoula, Richard Dobson, Harry Hemingway
Electronic health records (EHR) are increasingly being used for constructing disease risk prediction models.
no code implementations • 24 Jul 2017 • Vaclav Papez, Spiros Denaxas, Harry Hemingway
Electronic Health Records are electronic data generated during or as a byproduct of routine patient care.
no code implementations • 28 Feb 2017 • Henrietta Forssen, Riyaz S. Patel, Natalie Fitzpatrick, Aroon Hingorani, Adam Timmis, Harry Hemingway, Spiros C. Denaxas
Metabolomic data can potentially enable accurate, non-invasive and low-cost prediction of coronary artery disease.