Probabilistic detection of vital sign abnormality with Gaussian process regression

IEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012

Volumn , Issue , 2012, Pages 187-192

  Wong, David ^a   Clifton, David A ^a   Tarassenko, Lionel ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Data Fusion; Gaussian Process; Novelty Detection; Patient Monitoring

Indexed keywords

AMBULATORY MONITORING; AUTOMATED DATA; CLINICAL STUDY; DATA FUSION ALGORITHM; DATA-FUSION SYSTEMS; EARLY WARNING SYSTEM; GAUSSIAN PROCESS REGRESSION; GAUSSIAN PROCESSES; HOSPITAL SETTINGS; MISSING DATA; MONITORING SYSTEM; NOVELTY DETECTION; PATIENT MOVEMENT; VITAL SIGN;

BIOINFORMATICS; DATA FUSION; GAUSSIAN NOISE (ELECTRONIC); HOSPITALS; PATIENT MONITORING; PATIENT TREATMENT; UNCERTAINTY ANALYSIS;

GAUSSIAN DISTRIBUTION;

EID: 84872836902     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBE.2012.6399671     Document Type: Conference Paper

References (17)

1. R. M. Schein, N. Hazday, M. Pena, B.H. Ruben and C.L. Sprung. Clinical antecedents to in-hospital cardiopulmonary arrest. Chest, vol. 98, pp.1388-1392, 1990.
2. National Patient Safety Association, "Safer care for acutely ill patients: Learning from serious accidents," Technical Report, 2007.
3. L. Tarassenko, D. Clifton, M. Pinsky, M.Hravnak, J. Woods, and P. Watkinson, "Centile-based early warning scores derived from statistical distributions of vital signs," Resuscitation, vol. 82, no. 8, pp. 1013-1018, 2011.
4. D. Prytherch, G. Smith, P Schmidt, and P Featherstone. ViEWS - Towards a national early warning score for detecting adult inpatient deterioration. Resuscitation, vol. 81, no. 8, pp 932-937, 2010
5. Y. Lin, I. Jan, P. Ko, Y. Chen, J. Wong and G. Jan. A wireless PDA-based physiological monitoring system for patient transport. IEEE Trans. Information Technology in Biomedicine. Vol. 8, pp. 439-447, 2004.
6. L. Tarassenko, A. Hann and D. Young. Integrated monitoring and analysis for early warning of patient deterioration. British Journal of Anaesthesia. Vol. 97, p. 64, 2006.
7. C. Tsien and J Fackler. Poor prognosis for existing monitors in the intensive care unit. Critical Care Medicine, Vol. 25, No. 4, p. 466, 1997
8. C. Oberli, J Urzua, C. Saez, M. Guarini, A.Cipriano, B. Garayar, G. Lema, R. Canessa, C.Sacco, and M. Irarrazeval. An expert system for monitoring alarm integration. Journal of clinical monitoring and computing, Vol. 15, pp. 29-35, 1999
9. R. Schoenberg, D. Sands, and C. Safran. Making ICU alarms meaningful: a comparison of traditional vs. trend-based algorithms. In 1999 Proc AMIA symposium. P. 379
10. Y. Zhang. Real-time development of patient-specific alarm algorithms for critical care. In 2007 Proc. Engineering in Medicine and Biology Society, pp. 4351-4354
11. S. Charbonnier and S. Gentil. A trend-based alarm system to improve patient monitoring in intensive care units. Control Engineering Practice, Vol. 15, No. 9, pp. 1039-1050, 2007
12. S. Hoare and P. Beatty. Automatic artifact identification in anaesthesia patient record keeping: a comparison of techniques. Medical engineering & physics. Vol. 22, No. 8, pp. 547-553, 2000
13. C. Williams, J. Quinn, and N. McIntosh. Factorial switching Kalman filters for condition monitoring in neonatal intensive care. Advances in Neural Information Processing Systems, Vol. 18, p. 1513, 2006
14. rd IEE International Seminar on Medical Application of Signal Processing, pp. 71-76
15. O. Stegle, S Fallert, D MacKay, and S. Brage. Gaussian process robust regression for noisy heart rate data. IEEE Trans. Biomedical Engineering. Vol. 55, pp 2143-2151, 2008.
16. S. Wilson, D. Wong, D. Clifton, S. Fleming, R. Way, R. Pullinger and L. Tarrasenko. Track and trigger in an Emergency department: an observational evaluation study. Emergency Medicine Journal, in press.
17. P. Boyle and M. Frean. Dependent Gaussian processes. In 2005 Proc. Advances in neural information processing systems 17. Vol. 17, p. 217