Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU

  • Gal Hever
  • Liel Cohen
  • Michael F. O’Connor
  • Idit Matot
  • Boaz Lerner
  • Yuval BitanEmail author
Original Research


Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs monitors ranges from 0.72 to 0.99. We applied machine learning (ML) to ICU multi-sensor information to imitate a medical specialist in diagnosing patient condition. We hypothesized that applying this data-driven approach to medical monitors will help reduce the FAR even when data from sensors are missing. An expert-based rules algorithm identified and tagged in our dataset seven clinical alarm scenarios. We compared a random forest (RF) ML model trained using the tagged data, where parameters (e.g., heart rate or blood pressure) were (deliberately) removed, in detecting ICU signals with the full expert-based rules (FER), our ground truth, and partial expert-based rules (PER), missing these parameters. When all alarm scenarios were examined, RF and FER were almost identical. However, in the absence of one to three parameters, RF maintained its values of the Youden index (0.94–0.97) and positive predictive value (PPV) (0.98–0.99), whereas PER lost its value (0.54–0.8 and 0.76–0.88, respectively). While the FAR for PER with missing parameters was 0.17–0.39, it was only 0.01–0.02 for RF. When scenarios were examined separately, RF showed clear superiority in almost all combinations of scenarios and numbers of missing parameters. When sensor data are missing, specialist performance worsens with the number of missing parameters, whereas the RF model attains high accuracy and low FAR due to its ability to fuse information from available sensors, compensating for missing parameters.


False alarms Intensive care unit Machine learning Missing data Random forest 


Compliance with ethical standards

Conflict of interest

The authors decalre that they have no conflict of interest.


  1. 1.
    Drew BJ, Califf RM, Funk M, Kaufman ES, Krucoff MW, Laks MM, et al. Practice standards for electrocardiographic monitoring in hospital settings. Circulation. 2004;110(17):2721–46.CrossRefGoogle Scholar
  2. 2.
    Sendelbach S, Funk M. Alarm fatigue: a patient safety concern. Adv Crit Care. 2013;24(4):378–86.CrossRefGoogle Scholar
  3. 3.
    Drew BJ, Harris P, Schindler D, Salas-Boni R, Bai Y, Tinoco A, et al. Insights into the problem of alarm fatigue with physiologic monitor devices: a comprehensive observational study of consecutive intensive care unit patients. PLoS ONE. 2014;9(10):1–23.CrossRefGoogle Scholar
  4. 4.
    Cvach M. Monitor alarm fatigue: an integrative review. Biomed Instrum Technol. 2012;46(4):268–77.CrossRefGoogle Scholar
  5. 5.
    Sorkin RD. FORUM: why are people turning off our alarms? J Acoust Soc Am. 1988;84(3):1107–8.CrossRefGoogle Scholar
  6. 6.
    Edworthy J. The design and implementation of non-verbal auditory warnings. Appl Ergon. 1994;25(4):202–10.CrossRefGoogle Scholar
  7. 7.
    Xie H, Kang J, Mills GH. Clinical review: the impact of noise on patients’ sleep and the effectiveness of noise reduction strategies in intensive care units. Crit Care. 2009;13(2):208.CrossRefGoogle Scholar
  8. 8.
    Institute ECRI. Top 10 heath technology hazards for 2012. Health Devices. 2011;40(11):358–73.Google Scholar
  9. 9.
    Institute ECRI. Top 10 health technology hazards for 2013. Health Devices. 2012;41(11):342–65.Google Scholar
  10. 10.
    Institute ECRI. Top 10 heath technology hazards for 2014. Health Devices. 2013;42(11):354–80.Google Scholar
  11. 11.
    ECRI Institute. Top 10 heath technology hazards for 2015. Health Devices. 2014.Google Scholar
  12. 12.
    ECRI Institute. Top 10 heath technology hazards for 2016. Health Devices. 2015.Google Scholar
  13. 13.
    ECRI Institute. Top 10 heath technology hazards for 2017. Health Devices. 2016.Google Scholar
  14. 14.
    Clifford GD, Silva I, Moody B, Li Q, Kella D, Shahin A, et al. The PhysioNet/Computing in cardiology challenge 2015: reducing false arrhythmia alarms in the ICU. Comput Cardiol. 2015;2015:273–6.Google Scholar
  15. 15.
    Eerikäinen LM, Vanschoren J, Rooijakkers MJ, Vullings R, Aarts RM. Reduction of false arrhythmia alarms using signal selection and machine learning. Physiol Meas. 2016;37(8):204.CrossRefGoogle Scholar
  16. 16.
    Rijsbergen CJ. Information Retrieval. 2nd ed. London: Butterworths; 1979.Google Scholar
  17. 17.
    Bitan Y, O’Connor MF. Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment. F1000Research. 2012;1:45.CrossRefGoogle Scholar
  18. 18.
    Imhoff M, Kuhls S. Alarm algorithms in critical care monitoring. Anesth Analg. 2006;102(5):1525–37.CrossRefGoogle Scholar
  19. 19.
    Vesin A, Azoulay E, Ruckly S, Vignoud L, Rusinovà K, Benoit D, et al. Reporting and handling missing values in clinical studies in intensive care units. Intensive Care Med. 2013;39(8):1396–404.CrossRefGoogle Scholar
  20. 20.
    Altman DG, Bland JM. Statistics notes: diagnostic tests 2: predictive values. Br Med J. 1994;30(6947):102.CrossRefGoogle Scholar
  21. 21.
    Lalkhen AG, McCluskey A. Clinical tests: sensitivity and specificity. Contin Educ Anaesth Crit Care Pain. 2008;8(6):221–3.CrossRefGoogle Scholar
  22. 22.
    Božikov J, Zaletel-Kragelj L. Test validity measures and receiver operating characteristic (ROC) analysis. Methods Tools Public Health. 2010;50:749–70.Google Scholar
  23. 23.
    Bishop CM. Pattern recognition and machine learning. New York: Springer; 2007.Google Scholar
  24. 24.
    Liaw A, Wiener M. Classification and regression by random forest. R News. 2002;2(3):18–22.Google Scholar
  25. 25.
    Breiman L. Classification and regression trees. Belmont: Wadsworth International Group; 1984.Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Industrial Engineering and ManagementBen-Gurion University of the NegevBeer ShevaIsrael
  2. 2.Department of Anesthesia and Critical CareThe University of ChicagoChicagoUSA
  3. 3.Department of Anesthesia and Critical CareTel-Aviv Medical CenterTel-AvivIsrael

Personalised recommendations