A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier
Abstract
Purpose: We report that 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: Lastly, 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.
- Authors:
-
[1];
[2]
- Univ. of Tennessee, Knoxville, TN (United States)
- University of Tennessee Health, Memphis, TN (United States)
- Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1502531
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- International Journal of Medical Informatics
- Additional Journal Information:
- Journal Volume: 122; Journal Issue: C; Journal ID: ISSN 1386-5056
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 60 APPLIED LIFE SCIENCES; 97 MATHEMATICS AND COMPUTING; 59 BASIC BIOLOGICAL SCIENCES
Citation Formats
van Wyk, Franco, Khojandi, Anahita, Mohammed, Akram, Begoli, Edmon, Davis, Robert L., and Kamaleswaran, Rishikesan. A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier. United States: N. p., 2018.
Web. doi:10.1016/j.ijmedinf.2018.12.002.
van Wyk, Franco, Khojandi, Anahita, Mohammed, Akram, Begoli, Edmon, Davis, Robert L., & Kamaleswaran, Rishikesan. A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier. United States. https://doi.org/10.1016/j.ijmedinf.2018.12.002
van Wyk, Franco, Khojandi, Anahita, Mohammed, Akram, Begoli, Edmon, Davis, Robert L., and Kamaleswaran, Rishikesan. Mon .
"A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier". United States. https://doi.org/10.1016/j.ijmedinf.2018.12.002. https://www.osti.gov/servlets/purl/1502531.
@article{osti_1502531,
title = {A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier},
author = {van Wyk, Franco and Khojandi, Anahita and Mohammed, Akram and Begoli, Edmon and Davis, Robert L. and Kamaleswaran, Rishikesan},
abstractNote = {Purpose: We report that 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: Lastly, 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.},
doi = {10.1016/j.ijmedinf.2018.12.002},
journal = {International Journal of Medical Informatics},
number = C,
volume = 122,
place = {United States},
year = {Mon Dec 10 00:00:00 EST 2018},
month = {Mon Dec 10 00:00:00 EST 2018}
}
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Works referenced in this record:
A New Initiative on Precision Medicine
journal, February 2015
- Collins, Francis S.; Varmus, Harold
- New England Journal of Medicine, Vol. 372, Issue 9
Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database
conference, July 2017
- Hung, Chen-Ying; Chen, Wei-Chen; Lai, Po-Tsun
- 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Precision Medicine—Personalized, Problematic, and Promising
journal, January 2015
- Larry Jameson, J.; Longo, Dan L.
- Obstetrical & Gynecological Survey, Vol. 70, Issue 10
Big data analytics in healthcare: promise and potential
journal, February 2014
- Raghupathi, Wullianallur; Raghupathi, Viju
- Health Information Science and Systems, Vol. 2, Issue 1
Big Data in Healthcare
journal, January 2016
- Roesems-Kerremans, Gisele
- Journal of Healthcare Communications, Vol. 01, Issue 04
Specific Etiologies Associated With the Multiple Organ Dysfunction Syndrome in Children: Part 1
journal, January 2017
- Upperman, Jeffrey S.; Lacroix, Jacques; Curley, Martha A. Q.
- Pediatric Critical Care Medicine, Vol. 18
Clinical Mimics: An Emergency Medicine–Focused Review of Sepsis Mimics
journal, January 2017
- Long, Brit; Koyfman, Alex
- The Journal of Emergency Medicine, Vol. 52, Issue 1
Assessing available information on the burden of sepsis: global estimates of incidence, prevalence and mortality
journal, June 2012
- Jawad, Issrah; Lukšić, Ivana; Rafnsson, Snorri Bjorn
- Journal of Global Health, Vol. 2, Issue 1
Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics
journal, November 2017
- Shashikumar, Supreeth P.; Stanley, Matthew D.; Sadiq, Ismail
- Journal of Electrocardiology, Vol. 50, Issue 6
Reduction of false arrhythmia alarms using signal selection and machine learning
journal, July 2016
- Eerikäinen, Linda M.; Vanschoren, Joaquin; Rooijakkers, Michael J.
- Physiological Measurement, Vol. 37, Issue 8
Predicting disease risks from highly imbalanced data using random forest
journal, July 2011
- Khalilia, Mohammed; Chakraborty, Sounak; Popescu, Mihail
- BMC Medical Informatics and Decision Making, Vol. 11, Issue 1
A survey on deep learning in medical image analysis
journal, December 2017
- Litjens, Geert; Kooi, Thijs; Bejnordi, Babak Ehteshami
- Medical Image Analysis, Vol. 42
Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features
journal, October 2014
- Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay
- Journal of Medical Imaging, Vol. 1, Issue 3
A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy
journal, June 2016
- Zhang, Yudong; Sun, Yi; Phillips, Preetha
- Journal of Medical Systems, Vol. 40, Issue 7
Learning representations for the early detection of sepsis with deep neural networks
journal, October 2017
- Kam, Hye Jin; Kim, Ha Young
- Computers in Biology and Medicine, Vol. 89
A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length
journal, March 2018
- Kamaleswaran, Rishikesan; Mahajan, Ruhi; Akbilgic, Oguz
- Physiological Measurement, Vol. 39, Issue 3
How much data should we collect? A case study in sepsis detection using deep learning
conference, November 2017
- van Wyk, Franco; Khojandi, Anahita; Kamaleswaran, Rishikesan
- 2017 IEEE Healthcare Innovations and Point-of-Care Technologies (HI-POCT), 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)
An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU
journal, January 2018
- Nemati, Shamim; Holder, Andre; Razmi, Fereshteh
- Critical Care Medicine, Vol. 46, Issue 4
A targeted real-time early warning score (TREWScore) for septic shock
journal, August 2015
- Henry, Katharine E.; Hager, David N.; Pronovost, Peter J.
- Science Translational Medicine, Vol. 7, Issue 299
Abnormal Heart Rate Characteristics Preceding Neonatal Sepsis and Sepsis-Like Illness
journal, June 2003
- Griffin, M. Pamela; O'Shea, T. Michael; Bissonette, Eric A.
- Pediatric Research, Vol. 53, Issue 6
Continuous Multi-Parameter Heart Rate Variability Analysis Heralds Onset of Sepsis in Adults
journal, August 2009
- Ahmad, Saif; Ramsay, Tim; Huebsch, Lothar
- PLoS ONE, Vol. 4, Issue 8
Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU*
journal, January 2018
- Kamaleswaran, Rishikesan; Akbilgic, Oguz; Hallman, Madhura A.
- Pediatric Critical Care Medicine, Vol. 19, Issue 10
Raising concerns about the Sepsis-3 definitions
journal, January 2018
- Sartelli, Massimo; Kluger, Yoram; Ansaloni, Luca
- World Journal of Emergency Surgery, Vol. 13, Issue 1
Comparison of WBC, ESR, CRP and PCT serum levels in septic and non-septic burn cases
journal, September 2008
- Barati, Mitra; Alinejad, Faranak; Bahar, Mohammad Ali
- Burns, Vol. 34, Issue 6
Classification and Regression Trees.
journal, September 1984
- Gordon, A. D.; Breiman, L.; Friedman, J. H.
- Biometrics, Vol. 40, Issue 3
Deep learning
journal, May 2015
- LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
- Nature, Vol. 521, Issue 7553
Python for Scientific Computing
journal, January 2007
- Oliphant, Travis E.
- Computing in Science & Engineering, Vol. 9, Issue 3
Works referencing / citing this record:
Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients
journal, August 2019
- Mohammed, Akram; Cui, Yan; Mas, Valeria R.
- Scientific Reports, Vol. 9, Issue 1
Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread
journal, June 2019
- Cartelle Gestal, Mónica; Dedloff, Margaret R.; Torres-Sangiao, Eva
- Applied Sciences, Vol. 9, Issue 12
Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy
journal, January 2020
- Fleuren, Lucas M.; Klausch, Thomas L. T.; Zwager, Charlotte L.
- Intensive Care Medicine, Vol. 46, Issue 3
Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study
journal, January 2020
- Mohammed, Akram; Podila, Pradeep S. B.; Davis, Robert L.
- Journal of Medical Internet Research, Vol. 22, Issue 5
Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy
text, January 2020
- Fleuren, Lucas M.; Klausch, Thomas L. T.; Zwager, Charlotte L.
- Apollo - University of Cambridge Repository
Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy
text, January 2020
- Fleuren, Lucas M.; Klausch, Thomas L. T.; Zwager, Charlotte L.
- Apollo - University of Cambridge Repository
Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.
text, January 2020
- Fleuren, Lucas M.; Lt, Klausch, Thomas; Zwager, Charlotte L.
- Apollo - University of Cambridge Repository