Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
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Title
Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
Authors
Keywords
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Journal
Nature Biomedical Engineering
Volume -, Issue -, Pages -
Publisher
Springer Nature America, Inc
Online
2018-09-28
DOI
10.1038/s41551-018-0304-0
References
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Related references
Note: Only part of the references are listed.- Surgical data science for next-generation interventions
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- Machine Learning in Medicine
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- Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy
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- Estimate of the global volume of surgery in 2012: an assessment supporting improved health outcomes
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- Learning (predictive) risk scores in the presence of censoring due to interventions
- (2015) Kirill Dyagilev et al. MACHINE LEARNING
- A targeted real-time early warning score (TREWScore) for septic shock
- (2015) Katharine E. Henry et al. Science Translational Medicine
- Perioperative hypoxemia is common with horizontal positioning during general anesthesia and is associated with major adverse outcomes: a retrospective study of consecutive patients
- (2014) C Michael Dunham et al. BMC Anesthesiology
- Explaining prediction models and individual predictions with feature contributions
- (2013) Erik Štrumbelj et al. KNOWLEDGE AND INFORMATION SYSTEMS
- The incidence of hypoxemia during surgery: evidence from two institutions
- (2010) Jesse M. Ehrenfeld et al. Canadian Journal of Anesthesia-Journal canadien d anesthesie
- Integration of Early Physiological Responses Predicts Later Illness Severity in Preterm Infants
- (2010) S. Saria et al. Science Translational Medicine
- Decision Support Increases Guideline Adherence for Prescribing Postoperative Nausea and Vomiting Prophylaxis
- (2009) Fabian O. Kooij et al. ANESTHESIA AND ANALGESIA
- Anesthesia Awareness and the Bispectral Index
- (2008) Michael S. Avidan et al. NEW ENGLAND JOURNAL OF MEDICINE
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