Obesity Cohorts Based on Comorbidities Extracted from Clinical Notes

  • Ruth Reátegui
  • Sylvie Ratté
  • Estefanía Bautista-Valarezo
  • Victor Duque
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)


Clinical notes provide a comprehensive and overall impression of the patient’s health. However, the automatic extraction of information within these notes is challenging due to their narrative style. In this context, our goal was two-fold: first, extracting fourteen comorbidities related to obesity automatically from i2b2 Obesity Challenge data using the MetaMap tool; and second, identify patients’ cohorts applying sparse K-means algorithms on the extracted data. The results showed an average of 0.86 for recall, 0.94 for precision, and 0.89 for F-score. Also, three types of cohorts were found. The results showed that MetaMap can represent a good strategy for automatically extracting medical entities such as diseases or syndromes. Moreover, three types of cohorts could be identified based on the number of comorbidities and the percentage of patients suffering from them. These results show that hypertension, diabetes, CAD, CHF, HCL, OSA, asthma, and GERD were the most prevalent diseases.


Obesity Clinical notes MetaMap Patient cohorts Cluster analysis 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ruth Reátegui
    • 1
    • 2
  • Sylvie Ratté
    • 1
  • Estefanía Bautista-Valarezo
    • 2
  • Victor Duque
    • 2
  1. 1.École de technologie supérieureMontrealCanada
  2. 2.Universidad Técnica Particular de LojaLojaEcuador

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