A machine learning tool for identifying non-metastatic colorectal cancer in primary care

This publication has 27 references indexed in Scilit:

• Optimising the use of electronic health records to estimate the incidence of rheumatoid arthritis in primary care: what information is hidden in free text?
  BMC Medical Research Methodology, 2013
• High prevalence of diagnosis of diabetes, depression, anxiety, hypertension, asthma and COPD in the total population of Stockholm, Sweden – a challenge for public health
  BMC Public Health, 2013
• Symptoms and risk factors to identify women with suspected cancer in primary care: derivation and validation of an algorithm
  British Journal of General Practice, 2013
• Symptoms and risk factors to identify men with suspected cancer in primary care: derivation and validation of an algorithm
  British Journal of General Practice, 2013
• The diagnostic performance of scoring systems to identify symptomatic colorectal cancer compared to current referral guidance
  Gut, 2011
• The completeness of the Swedish Cancer Register – a sample survey for year 1998
  Acta Oncologica, 2009
• Clinical features of colorectal cancer before emergency presentation: a population-based case-control study
  Family Practice, 2006
• Greedy function approximation: A gradient boosting machine.
  The Annals of Statistics, 2001
• Colorectal cancer and polyps in patients aged 40 years and over who consult a GP with rectal bleeding
  Family Practice, 1996
• Predictive value of signs and symptoms for colorectal cancer in patients with rectal bleeding in general practice
  Family Practice, 1995