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Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks

Tom Van Steenkiste (UGent) , Joeri Ruyssinck (UGent) , Leen De Baets (UGent) , Johan Decruyenaere (UGent) , Filip De Turck (UGent) , Femke Ongenae (UGent) and Tom Dhaene (UGent)
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Abstract
Introduction: Blood cultures are often performed in the intensive care unit (ICU) to detect bloodstream infections and identify pathogen type, further guiding treatment. Early detection is essential, as a bloodstream infection can give cause to sepsis, a severe immune response associated with an increased risk of organ failure and death. Problem statement The early clinical detection of a bloodstream infection is challenging but rapid targeted treatment, within the first place antimicrobials, substantially increases survival chances. As blood cultures require time to incubate, early clinical detection using physiological signals combined with indicative lab values is pivotal. Objective: In this work, a novel method is constructed and explored for the potential prediction of the outcome of a blood culture test. The approach is based on a temporal computational model which uses nine clinical parameters measured over time. Methodology: We use a bidirectional long short-term memory neural network, a type of recurrent neural network well suited for tasks where the time lag between a predictive event and outcome is unknown. Evaluation is performed using a novel high-quality database consisting of 2177 ICU admissions at the Ghent University Hospital located in Belgium. Results: The network achieves, on average, an area under the receiver operating characteristic curve of 0.99 and an area under the precision-recall curve of 0.82. In addition, our results show that predicting several hours upfront is possible with only a small decrease in predictive power. In this setting, it outperforms traditional non-temporal, machine learning models. Conclusion: Our proposed computational model accurately predicts the outcome of blood culture tests using nine clinical parameters. Moreover, it can be used in the ICU as an early warning system to detect patients at risk of blood stream infection.
Keywords
Medicine (miscellaneous), Artificial Intelligence, SEVERE SEPSIS, MORTALITY

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MLA
Van Steenkiste, Tom et al. “Accurate Prediction of Blood Culture Outcome in the Intensive Care Unit Using Long Short-term Memory Neural Networks.” ARTIFICIAL INTELLIGENCE IN MEDICINE 97 (2019): 38–43. Print.
APA
Van Steenkiste, T., Ruyssinck, J., De Baets, L., Decruyenaere, J., De Turck, F., Ongenae, F., & Dhaene, T. (2019). Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks. ARTIFICIAL INTELLIGENCE IN MEDICINE, 97, 38–43.
Chicago author-date
Van Steenkiste, Tom, Joeri Ruyssinck, Leen De Baets, Johan Decruyenaere, Filip De Turck, Femke Ongenae, and Tom Dhaene. 2019. “Accurate Prediction of Blood Culture Outcome in the Intensive Care Unit Using Long Short-term Memory Neural Networks.” Artificial Intelligence in Medicine 97: 38–43.
Chicago author-date (all authors)
Van Steenkiste, Tom, Joeri Ruyssinck, Leen De Baets, Johan Decruyenaere, Filip De Turck, Femke Ongenae, and Tom Dhaene. 2019. “Accurate Prediction of Blood Culture Outcome in the Intensive Care Unit Using Long Short-term Memory Neural Networks.” Artificial Intelligence in Medicine 97: 38–43.
Vancouver
1.
Van Steenkiste T, Ruyssinck J, De Baets L, Decruyenaere J, De Turck F, Ongenae F, et al. Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks. ARTIFICIAL INTELLIGENCE IN MEDICINE. 2019;97:38–43.
IEEE
[1]
T. Van Steenkiste et al., “Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks,” ARTIFICIAL INTELLIGENCE IN MEDICINE, vol. 97, pp. 38–43, 2019.
@article{8585847,
  abstract     = {Introduction: Blood cultures are often performed in the intensive care unit (ICU) to detect bloodstream infections and identify pathogen type, further guiding treatment. Early detection is essential, as a bloodstream infection can give cause to sepsis, a severe immune response associated with an increased risk of organ failure and death. Problem statement The early clinical detection of a bloodstream infection is challenging but rapid targeted treatment, within the first place antimicrobials, substantially increases survival chances. As blood cultures require time to incubate, early clinical detection using physiological signals combined with indicative lab values is pivotal.
Objective: In this work, a novel method is constructed and explored for the potential prediction of the outcome of a blood culture test. The approach is based on a temporal computational model which uses nine clinical parameters measured over time.
Methodology: We use a bidirectional long short-term memory neural network, a type of recurrent neural network well suited for tasks where the time lag between a predictive event and outcome is unknown. Evaluation is performed using a novel high-quality database consisting of 2177 ICU admissions at the Ghent University Hospital located in Belgium.
Results: The network achieves, on average, an area under the receiver operating characteristic curve of 0.99 and an area under the precision-recall curve of 0.82. In addition, our results show that predicting several hours upfront is possible with only a small decrease in predictive power. In this setting, it outperforms traditional non-temporal, machine learning models.
Conclusion: Our proposed computational model accurately predicts the outcome of blood culture tests using nine clinical parameters. Moreover, it can be used in the ICU as an early warning system to detect patients at risk of blood stream infection.},
  author       = {Van Steenkiste, Tom and Ruyssinck, Joeri and De Baets, Leen and Decruyenaere, Johan and De Turck, Filip and Ongenae, Femke and Dhaene, Tom},
  issn         = {0933-3657},
  journal      = {ARTIFICIAL INTELLIGENCE IN MEDICINE},
  keywords     = {Medicine (miscellaneous),Artificial Intelligence,SEVERE SEPSIS,MORTALITY},
  language     = {eng},
  pages        = {38--43},
  title        = {Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks},
  url          = {http://dx.doi.org/10.1016/j.artmed.2018.10.008},
  volume       = {97},
  year         = {2019},
}

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