Finding Cervical Cancer Symptoms in Swedish Clinical Text using a Machine Learning Approach and NegEx.

Journal: AMIA ... Annual Symposium proceedings. AMIA Symposium
Published Date: Nov 5, 2015

Abstract

Detection of early symptoms in cervical cancer is crucial for early treatment and survival. To find symptoms of cervical cancer in clinical text, Named Entity Recognition is needed. In this paper the Clinical Entity Finder, a machine-learning tool trained on annotated clinical text from a Swedish internal medicine emergency unit, is evaluated on cervical cancer records. The Clinical Entity Finder identifies entities of the types body part, finding and disorder and is extended with negation detection using the rule-based tool NegEx, to distinguish between negated and non-negated entities. To measure the performance of the tools on this new domain, two physicians annotated a set of clinical notes from the health records of cervical cancer patients. The inter-annotator agreement for finding, disorder and body part obtained an average F-score of 0.677 and the Clinical Entity Finder extended with NegEx had an average F-score of 0.667.

Authors

  • Rebecka Weegar
    Department of Computer and Systems Sciences, (DSV), Stockholm University, Sweden.
  • Maria Kvist
    Dept. of Computer and Systems Sciences, Stockholm University, Sweden; Dept. of Learning, Informatics, Management and Ethics, Karolinska Institute, Sweden.
  • Karin Sundström
    Department of Laboratory medicine (LABMED), Karolinska Institutet, Stockholm, Sweden.
  • Søren Brunak
    NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
  • Hercules Dalianis
    Department of Computer and Systems Sciences, (DSV), Stockholm University, Sweden.

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