Skip to main content
Journal cover image

A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier.

Publication ,  Journal Article
van Wyk, F; Khojandi, A; Mohammed, A; Begoli, E; Davis, RL; Kamaleswaran, R
Published in: Int J Med Inform
February 2019

PURPOSE: Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. To improve short- and long-term outcomes, it is critical to detect at-risk sepsis patients at an early stage. METHODS: A data-set consisting of high-frequency physiological data from 1161 critically ill patients was analyzed. 377 patients had developed sepsis, and had data at least 3 h prior to the onset of sepsis. A random forest classifier was trained to discriminate between sepsis and non-sepsis patients in real-time using a total of 132 features extracted from a moving time-window. The model was trained on 80% of the patients and was tested on the remaining 20% of the patients, for two observational periods of lengths 3 and 6 h prior to onset. RESULTS: The model that used continuous physiological data alone resulted in sensitivity and F1 score of up to 80% and 67% one hour before sepsis onset. On average, these models were able to predict sepsis 294.19 ± 6.50 min (5 h) before the onset. CONCLUSIONS: The use of machine learning algorithms on continuous streams of physiological data can allow for early identification of at-risk patients in real-time with high accuracy.

Duke Scholars

Published In

Int J Med Inform

DOI

EISSN

1872-8243

Publication Date

February 2019

Volume

122

Start / End Page

55 / 62

Location

Ireland

Related Subject Headings

  • Young Adult
  • Sepsis
  • Retrospective Studies
  • Models, Cardiovascular
  • Middle Aged
  • Medical Informatics
  • Male
  • Machine Learning
  • Intensive Care Units
  • Humans
 

Citation

APA
Chicago
ICMJE
MLA
NLM
van Wyk, F., Khojandi, A., Mohammed, A., Begoli, E., Davis, R. L., & Kamaleswaran, R. (2019). A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier. Int J Med Inform, 122, 55–62. https://doi.org/10.1016/j.ijmedinf.2018.12.002
Wyk, Franco van, Anahita Khojandi, Akram Mohammed, Edmon Begoli, Robert L. Davis, and Rishikesan Kamaleswaran. “A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier.Int J Med Inform 122 (February 2019): 55–62. https://doi.org/10.1016/j.ijmedinf.2018.12.002.
van Wyk F, Khojandi A, Mohammed A, Begoli E, Davis RL, Kamaleswaran R. A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier. Int J Med Inform. 2019 Feb;122:55–62.
van Wyk, Franco, et al. “A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier.Int J Med Inform, vol. 122, Feb. 2019, pp. 55–62. Pubmed, doi:10.1016/j.ijmedinf.2018.12.002.
van Wyk F, Khojandi A, Mohammed A, Begoli E, Davis RL, Kamaleswaran R. A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier. Int J Med Inform. 2019 Feb;122:55–62.
Journal cover image

Published In

Int J Med Inform

DOI

EISSN

1872-8243

Publication Date

February 2019

Volume

122

Start / End Page

55 / 62

Location

Ireland

Related Subject Headings

  • Young Adult
  • Sepsis
  • Retrospective Studies
  • Models, Cardiovascular
  • Middle Aged
  • Medical Informatics
  • Male
  • Machine Learning
  • Intensive Care Units
  • Humans