Probabilistic detection of short events, with application to critical care monitoring

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Volumn , Issue , 2009, Pages 49-56

  Aleks, Norm ^a   Russell, Stuart ^a   Madden, Michael G ^b   Morabito, Diane ^c   Staudenmayer, Kristan ^d   Cohen, Mitchell ^c   Manley, Geoffrey ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b NATIONAL UNIVERSITY OF IRELAND   (Ireland)

^c STANFORD UNIVERSITY   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BLOOD PRESSURE; DECISION MAKING;

AUTOMATED DECISION MAKING; BLOOD PRESSURE SENSORS; CRITICAL CARE; DATA ARTIFACTS; FALSEALARMS; FIXED INTERVALS; PROBABILISTIC DETECTIONS; PROBABILISTIC INFERENCE; PROBABILISTIC MODELS; SENSORS DATA;

INTENSIVE CARE UNITS;

EID: 84858791287     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. Aliferis, C., & Cooper, G. (1996). A structurally and temporally extended Bayesian belief network model: Definitions, properties, and modeling techniques. Proc. Uncertainty in Artificial Intelligence (pp. 28-39).
2. Beinlich, I., Suermondt, H., Chavez, R., & Cooper, G. (1989). The ALARM monitoring system. Proc. Second European Conference on Artificial Intelligence in Medicine (pp. 247-256).
3. Gordon, N. J., Salmond, D., & Smith, A. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. Radar and Signal Processing, IEE Proceedings-F, 140, 107-113.
4. Heldt, T., Long, W., Verghese, G., Szolovits, P., & Mark, R. (2006). Integrating data, models, and reasoning in critical care. Proceedings of the 28th IEEE EMBS International Conference (pp. 350-353). (Pubitemid 46519870)
5. Rao, C. V., & Arkin, A. P. (2003). Stochastic chemical kinetics and the quasi-steady-state assumption: Application to the Gillespie algorithm. Journal of Chemical Physics, 18.
6. Russell, S. J., & Norvig, P. (2003). Artificial intelligence: A modern approach. Upper Saddle River, New Jersey: Prentice-Hall. 2nd edition.
7. Shachter, R. D. (1986). Evaluating influence diagrams. Operations Research, 34, 871-882.
8. Sieben, W., & Gather, U. (2007). Classifying alarms in intensive care-analogy to hypothesis testing. Lecture notes in computer science, 130-138. (Pubitemid 47545187)
9. Tsien, C. L., & Fackler, J. C. (1997). Poor prognosis for existing monitors in the intensive care unit. Critical Care Medicine, 25, 614-619. (Pubitemid 27193973)
10. Williams, C. K. I., Quinn, J., & McIntosh, N. (2005). Factorial switching Kalman filters for condition monitoring in neonatal intensive care. NIPS. Vancouver, Canada.