Early prediction of circulatory failure in the intensive care unit using machine learning

Hyland, Stephanie L.; Faltys, Martin; Hüser, Matthias; Lyu, Xin; Gumbsch, Thomas; Esteban, Cristóbal; Bock, Christian; Horn, Max; Moor, Michael; Rieck, Bastian; Zimmermann, Marc; Bodenham, Dean A.; Borgwardt, Karsten; Rätsch, Gunnar; Merz, Tobias M.

doi:10.1038/s41591-020-0789-4

Cited by 274 publications

(233 citation statements)

References 79 publications

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“…5 ). Coupling modern machine learning algorithms that do not rely on specific assumption like traditional statistical models ( 40 ) to high temporal resolution databases that store raw waveforms, future studies may be able to estimate to assess subtle changes in patients trajectories ( 41 ).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza

Sauthier¹,

Jouvet

Newhams

et al. 2020

Critical Care Explorations

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Section: Discussionmentioning

confidence: 99%

Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza

Sauthier¹,

Jouvet

Newhams

et al. 2020

Critical Care Explorations

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“…Models generate risk scores every hour and we calculate performance using 2 approaches. To assess global performance, similar to prior work, 13, 26 , 33 , 34 metrics are calculated using the maximum score within independent 12-h windows. True positives are high-risk scores during 12-h blocks immediately preceding a sepsis event.…”

Section: Methodsmentioning

confidence: 99%

Machine learning for early detection of sepsis: an internal and temporal validation study

et al. 2020

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Objective Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice. Materials and Methods We trained internally and temporally validated a deep learning model (multi-output Gaussian process and recurrent neural network [MGP–RNN]) to detect sepsis using encounters from adult hospitalized patients at a large tertiary academic center. Sepsis was defined as the presence of 2 or more systemic inflammatory response syndrome (SIRS) criteria, a blood culture order, and at least one element of end-organ failure. The training dataset included demographics, comorbidities, vital signs, medication administrations, and labs from October 1, 2014 to December 1, 2015, while the temporal validation dataset was from March 1, 2018 to August 31, 2018. Comparisons were made to 3 machine learning methods, random forest (RF), Cox regression (CR), and penalized logistic regression (PLR), and 3 clinical scores used to detect sepsis, SIRS, quick Sequential Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS). Traditional discrimination statistics such as the C-statistic as well as metrics aligned with operational implementation were assessed. Results The training set and internal validation included 42 979 encounters, while the temporal validation set included 39 786 encounters. The C-statistic for predicting sepsis within 4 h of onset was 0.88 for the MGP–RNN compared to 0.836 for RF, 0.849 for CR, 0.822 for PLR, 0.756 for SIRS, 0.619 for NEWS, and 0.481 for qSOFA. MGP–RNN detected sepsis a median of 5 h in advance. Temporal validation assessment continued to show the MGP–RNN outperform all 7 clinical risk score and machine learning comparisons. Conclusions We developed and validated a novel deep learning model to detect sepsis. Using our data elements and feature set, our modeling approach outperformed other machine learning methods and clinical scores.

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“…In our work each of the tree based models are ensembles in their own right. Other systems have also been studied recently proving the benefit of machine learning in healthcare [ 56 ]. In the financial domain, Krauss and co-workers [ 57 ] found that applying a higher level of ensemble proved to be a powerful model and we intend to investigate similar ensembles applied to physiology in future work, as highlighted in Fig.…”

Section: Discussionmentioning

confidence: 99%

Comparing regression and neural network techniques for personalized predictive analytics to promote lung protective ventilation in Intensive Care Units

Hagan

Gillan

Spence

et al. 2020

Computers in Biology and Medicine

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Mechanical ventilation is a lifesaving tool and provides organ support for patients with respiratory failure. However, injurious ventilation due to inappropriate delivery of high tidal volume can initiate or potentiate lung injury. This could lead to acute respiratory distress syndrome, longer duration of mechanical ventilation, ventilator associated conditions and finally increased mortality. In this study, we explore the viability and compare machine learning methods to generate personalized predictive alerts indicating violation of the safe tidal volume per ideal body weight (IBW) threshold that is accepted as the upper limit for lung protective ventilation (LPV), prior to application to patients. We process streams of patient respiratory data recorded per minute from ventilators in an intensive care unit and apply several state-of-the-art time series prediction methods to forecast the behavior of the tidal volume metric per patient, 1 hour ahead. Our results show that boosted regression delivers better predictive accuracy than other methods that we investigated and requires relatively short execution times. Long short-term memory neural networks can deliver similar levels of accuracy but only after much longer periods of data acquisition, further extended by several hours computing time to train the algorithm. Utilizing Artificial Intelligence, we have developed a personalized clinical decision support tool that can predict tidal volume behavior within accuracy and compare alerts recorded from a real world system to highlight that our models would have predicted violations 1 hour ahead and can therefore conclude that the algorithms can provide clinical decision support.

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Early prediction of circulatory failure in the intensive care unit using machine learning

Cited by 274 publications

References 79 publications

Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza

Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza

Machine learning for early detection of sepsis: an internal and temporal validation study

Comparing regression and neural network techniques for personalized predictive analytics to promote lung protective ventilation in Intensive Care Units

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