Extracting Diagnoses and Investigation Results from Unstructured Text in Electronic Health Records by Semi-Supervised Machine Learning

by Wang, Zhuoran, Shah, Anoop D., Tate, A. Rosemary, Denaxas, Spiros, Shawe-Taylor, John, Hemingway, Harry
Published 2012

How many mailouts? Could attempts to increase the response rate in the Iraq war cohort study be counterproductive?

by Tate, A Rosemary, Jones, Margaret, Hull, Lisa, Fear, Nicola T, Rona, Roberto, Wessely, Simon, Hotopf, Matthew
Published 2007

Using free text information to explore how and when GPs code a diagnosis of ovarian cancer: an observational study using primary care records of patients with ovarian cancer

by Tate, A Rosemary, Martin, Alexander G R, Ali, Aishath, Cassell, Jackie A
Published 2011

Optimising Use of Electronic Health Records to Describe the Presentation of Rheumatoid Arthritis in Primary Care: A Strategy for Developing Code Lists

by Nicholson, Amanda, Ford, Elizabeth, Davies, Kevin A., Smith, Helen E., Rait, Greta, Tate, A. Rosemary, Petersen, Irene, Cassell, Jackie
Published 2013

Exploiting the potential of large databases of electronic health records for research using rapid search algorithms and an intuitive query interface

by Tate, A Rosemary, Beloff, Natalia, Al-Radwan, Balques, Wickson, Joss, Puri, Shivani, Williams, Timothy, Van Staa, Tjeerd, Bleach, Adrian
Published 2014

Optimising the use of electronic health records to estimate the incidence of rheumatoid arthritis in primary care: what information is hidden in free text?

by Ford, Elizabeth, Nicholson, Amanda, Koeling, Rob, Tate, A Rosemary, Carroll, John, Axelrod, Lesley, Smith, Helen E, Rait, Greta, Davies, Kevin A, Petersen, Irene, Williams, Tim, Cassell, Jackie A
Published 2013