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On the Use of Decision Trees Based on Diagnosis and Drug Codes for Analyzing Chronic Patients

  • Cristina Soguero-RuizEmail author
  • Ana Alberca Díaz-Plaza
  • Pablo de Miguel Bohoyo
  • Javier Ramos-López
  • Manuel Rubio-Sánchez
  • Alberto Sánchez
  • Inmaculada Mora-Jiménez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10814)

Abstract

Diabetes mellitus (DM) and essential hypertension (EH) are chronic diseases more prevalent every year, both independently and jointly. To gain insights about the particularities of these chronic conditions, we study the use of decision trees as a tool for selecting discriminative features and making predictive analyses of the health status of this kind of chronic patients. We considered gender, age, ICD9 codes for diagnosis and ATC codes for drugs associated with the diabetic and/or hypertensive population linked to the University Hospital of Fuenlabrada (Madrid, Spain) during 2012. Results show a relationship among DM/EH and diseases/drugs related to the respiratory system, mental disorders, or the musculoskeletal system. We conclude that drugs are quite informative, collecting information about the disease when the diagnosis code is not registered. Regarding predictive analyses, when discriminating patients with EH-DM and just one of these chronic conditions, better accuracy is obtained for EH (85.4%) versus DM (80.1%).

Keywords

Chronic health status Diabetes Hypertension Feature selection Decision trees Diagnoses Drugs 

Notes

Acknowledgments

This work has been partly supported by Research Projects TEC2016-75361-R, TIN2014-62143-EXP, and TIN2015-70799-R from the Spanish Government, and Research Project DTS17/00158 from Carlos III Institute.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Rey Juan Carlos UniversityMadridSpain
  2. 2.University Hospital of FuenlabradaMadridSpain

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