@InProceedings{boytcheva-EtAl:2017:BioNLP1,
  author    = {Boytcheva, Svetla  and  Nikolova, Ivelina  and  Angelova, Galia  and  Angelov, Zhivko},
  title     = {Identification of Risk Factors in Clinical Texts through Association Rules},
  booktitle = {Proceedings of the Biomedical NLP Workshop associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {64--72},
  abstract  = {We describe a method which extracts Association Rules from texts in order to
	recognise verbalisations of risk factors. Usually some basic vocabulary about
	risk factors is known but medical conditions are expressed in clinical
	narratives with much higher variety. We propose an approach for data-driven
	learning of specialised  medical vocabulary which, once collected, enables
	early alerting of potentially affected patients. The method is illustrated by
	experimens with clinical records of patients with Chronic Obstructive Pulmonary
	Disease (COPD) and comorbidity of CORD, Diabetes Melitus and Schizophrenia. Our
	input data come from the Bulgarian Diabetic Register, which is built using a
	pseudonymised collection of outpatient records for about 500,000 diabetic
	patients. The generated Association Rules for CORD are analysed in the context
	of demographic, gender, and age information. Valuable anounts of meaningful
	words, signalling risk factors, are discovered with high precision and
	confidence.},
  url       = {https://doi.org/10.26615/978-954-452-044-1_009}
}