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Identification of Patient Deterioration in Vital-Sign Data using One-Class Support Vector Machines.

, , , and . FedCSIS, page 125-131. (2011)

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Automated Novelty Detection in Industrial Systems., , , and . Advances of Computational Intelligence in Industrial Systems, volume 116 of Studies in Computational Intelligence, (2008)Likelihood-based artefact detection in continuously-acquired patient vital signs., , , , and . EMBC, page 2146-2149. IEEE, (2017)Gaussian Processes for Personalized e-Health Monitoring With Wearable Sensors., , , , and . IEEE Trans. Biomed. Eng., 60 (1): 193-197 (2013)Vital-sign Data Fusion Models for Post-operative Patients., , , , and . BIOSIGNALS, page 410-413. SciTePress, (2012)An Extreme Function Theory for Novelty Detection., , , , and . J. Sel. Topics Signal Processing, 7 (1): 28-37 (2013)Modelling physiological deterioration in post-operative patient vital-sign data., , , , and . Medical Biol. Eng. Comput., 51 (8): 869-877 (2013)Combined Support Vector Novelty Detection for Multi-channel Combustion Data., , , and . ICNSC, page 495-500. IEEE, (2007)Optimising Classifiers for the Detection of Physiological Deterioration in Patient Vital-sign Data., , , and . BIOSIGNALS, page 425-428. SciTePress, (2011)Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors., , , , and . IEEE J. Biomed. Health Informatics, 18 (3): 722-730 (2014)Gaussian process regression in vital-sign early warning systems., , , , and . EMBC, page 6161-6164. IEEE, (2012)