Automatic Extraction and Aggregation of Diseases from Clinical Notes

  • Ruth Reátegui
  • Sylvie Ratté
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 721)


Clinical notes provide medical information about the patient’s health. The automatic extraction of this information is relevant in order to analyze patterns for grouping patients with similar characteristics. In this paper, we used MetaMap to extract diseases present in 412 discharge summaries of obesity patients. The UMLS intra-source vocabulary relationships were used to make automatic aggregation of diseases. The results showed an average of 0.81 for recall, 0.92 for precision, and 0.84 for F-score. Finally, with the diseases extracted and aggregated three sub-graphs were identified; they correspond to patients with sleep apnea, those with heart diseases, and those with communicable diseases.


Clinical notes UMLS MetaMap Clustering graphs 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.École de technologie supérieureMontrealCanada
  2. 2.Universidad Técnica Particular de LojaLojaEcuador

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