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Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

PLoS ONE, Vol.13(8), p.e0202344 [Peer Reviewed Journal]

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Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study

American Journal of Epidemiology, 2014, Vol. 179(6), pp.764-774 [Peer Reviewed Journal]

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The diagnosis, burden and prognosis of dementia: A record-linkage cohort study in England

PLoS ONE, Vol.13(6), p.e0199026 [Peer Reviewed Journal]

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Time spent at blood pressure target and the risk of death and cardiovascular diseases

PLoS ONE, Vol.13(9), p.e0202359 [Peer Reviewed Journal]

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Ethnicity and the first diagnosis of a wide range of cardiovascular diseases: Associations in a linked electronic health record cohort of 1 million patients

PLoS ONE, Vol.12(6), p.e0178945 [Peer Reviewed Journal]

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Identifying unmet clinical need in hypertrophic cardiomyopathy using national electronic health records

PLoS ONE, Vol.13(1), p.e0191214 [Peer Reviewed Journal]

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Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning

PLoS ONE, Vol.7(1), p.e30412 [Peer Reviewed Journal]

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Association between clinically recorded alcohol consumption and initial presentation of 12 cardiovascular diseases: population based cohort study using linked health records

BMJ, 22 March 2017, Vol.356 [Peer Reviewed Journal]

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Prognosis of undiagnosed chest pain: linked electronic health record cohort study

BMJ, 3 April 2017, Vol.357 [Peer Reviewed Journal]

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beta-Blockers and Mortality After Acute Myocardial Infarction in Patients Without Heart Failure or Ventricular Dysfunction

Journal of the American College of Cardiology, 2017, Vol.69(22), pp.2710-2720 [Peer Reviewed Journal]

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  1. Hemingway, Harry
  2. Hemingway, H
  3. Denaxas, Spiros
  4. Timmis, Adam
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