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Ambient Intelligence for Health

AmIHEALTH 2015: Ambient Intelligence for Health pp 77–90Cite as

Real-Time Decision Support Using Data Mining to Predict Blood Pressure Critical Events in Intensive Medicine Patients

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9456))

Abstract

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95 %.

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Acknowledgments

This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013 and the contract PTDC/EEI-SII/1302/2012 (INTCare II).

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Authors and Affiliations

  1. Algoritmi Centre, University of Minho, Braga, Portugal

    Filipe Portela, Manuel Filipe Santos, José Machado & António Abelha

  2. Serviço Cuidados Intensivos, Centro Hospitalar do Porto, Hospital Santo António, Porto, Portugal

    Fernando Rua & Álvaro Silva

  3. ESEIG, Porto Polytechnic, Porto, Portugal

    Filipe Portela

Authors
  1. Filipe Portela
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  2. Manuel Filipe Santos
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  3. José Machado
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  4. António Abelha
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  5. Fernando Rua
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  6. Álvaro Silva
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Corresponding author

Correspondence to Filipe Portela .

Editor information

Editors and Affiliations

  1. Castilla-La Mancha University, CIUDAD REAL, Ciudad Real, Spain

    José Bravo

  2. Castilla-La Mancha University, Ciudad Real, Spain

    Ramón Hervás

  3. Technological University of Panamá, Panama, Panama

    Vladimir Villarreal

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Portela, F., Santos, M.F., Machado, J., Abelha, A., Rua, F., Silva, Á. (2015). Real-Time Decision Support Using Data Mining to Predict Blood Pressure Critical Events in Intensive Medicine Patients. In: Bravo, J., Hervás, R., Villarreal, V. (eds) Ambient Intelligence for Health. AmIHEALTH 2015. Lecture Notes in Computer Science(), vol 9456. Springer, Cham. https://doi.org/10.1007/978-3-319-26508-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-26508-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26507-0

  • Online ISBN: 978-3-319-26508-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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