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Predicting readmissions, mortality, and infections in the ICU using Machine Learning Techniques

Author

Listed:
  • Álvaro Riascos
  • Natalia Serna
  • Marcela Granados
  • Fernando Rosso
  • Ramiro Guerrero

Abstract

Health care at the Intensive Care Unit (ICU) is both expensive for hospitals and strenuous for doctors. Early detection of risk factors associated to readmissions, mortality, and infections in the ICU, can improve patient care quality and reduce costs in the long-run. In this article we use machine learning techniques to predict those three outcomes using patient-level data of the ICU of a high complexity hospital in Colombia. Our results show that pathologies of the aorta, cancer, neurologic and respiratory diseases as well as invasive procedures such as dialysis, tracheostomy, and bronchoscopy are positively correlated to the probability of readmission, death, and catheter infections in the ICU. The area under the ROC curve for the first outcome ranges between 71 and 74%, for the second between 76 and 81%, and for the third between 88 and 92%. We estimate a model that competes against the APACHE II scoring system and achieve the same predictive power using less information about the patient.

Suggested Citation

  • Álvaro Riascos & Natalia Serna & Marcela Granados & Fernando Rosso & Ramiro Guerrero, 2016. "Predicting readmissions, mortality, and infections in the ICU using Machine Learning Techniques," Documentos de Trabajo 15074, Quantil.
    • Handle: RePEc:col:000508:015074
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    References listed on IDEAS

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    1. Mohsen Bayati & Mark Braverman & Michael Gillam & Karen M Mack & George Ruiz & Mark S Smith & Eric Horvitz, 2014. "Data-Driven Decisions for Reducing Readmissions for Heart Failure: General Methodology and Case Study," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-9, October.
    2. Omar Badawi & Michael J Breslow, 2012. "Readmissions and Death after ICU Discharge: Development and Validation of Two Predictive Models," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-15, November.
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    More about this item

    Keywords

    Intensive Care Unit; machine learning; readmissions; mortality; catheter infections.;
    All these keywords.

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