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dc.contributor.advisorWilliams, Chris
dc.contributor.advisorMurray-Smith, Roderick
dc.contributor.authorStanculescu, Ioan Anton
dc.date.accessioned2016-06-16T15:11:04Z
dc.date.available2016-06-16T15:11:04Z
dc.date.issued2015-11-26
dc.identifier.urihttp://hdl.handle.net/1842/15886
dc.description.abstractThe vital signs monitoring data of an infant receiving intensive care are a rich source of information about its health condition. One major concern about the state of health of such patients is the onset of neonatal sepsis, a life-threatening bloodstream infection. As early signs are subtle and current diagnosis procedures involve slow laboratory testing, sepsis detection based on the monitored physiological dynamics is a clinically significant task. This challenging problem can be thoroughly modelled as real-time inference within a machine learning framework. In this thesis, we develop probabilistic dynamical models centred around the goal of providing useful predictions about the onset of neonatal sepsis. This research is characterised by the careful incorporation of domain knowledge for the purpose of extracting the infant’s true physiology from the monitoring data. We make two main contributions. The first one is the formulation of sepsis detection as learning and inference in an Auto-Regressive Hidden Markov Model (AR-HMM). The model investigates the extent to which physiological events observed in the patient’s monitoring traces could be used for the early detection of neonatal sepsis. In addition, the proposed approach involves exact marginalisation over missing data at inference time. When applying the ARHMM on a real-world dataset, we found that it can produce effective predictions about the onset of sepsis. Second, both sepsis and clinical event detection are formulated as learning and inference in a Hierarchical Switching Linear Dynamical System (HSLDS). The HSLDS models dynamical systems where complex interactions between modes of operation can be represented as a twolevel hidden discrete hierarchical structure. For neonatal condition monitoring, the lower layer models clinical events and is controlled by upper layer variables with semantics sepsis/nonsepsis. The model parameterisation and estimation procedures are adapted to the specifics of physiological monitoring data. We demonstrate that the performance of the HSLDS for the detection of sepsis is not statistically different from the AR-HMM, despite the fact that the latter model is given “ground truth” annotations of the patient’s physiology.en
dc.contributor.sponsorEngineering and Physical Sciences Research Council (EPSRC)en
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.relation.hasversionIoan Stanculescu, Christopher K. I. Williams, and Yvonne Freer. Autoregressive Hidden Markov Models for the Early Detection of Neonatal Sepsis. Biomedical and Health Informatics, IEEE Journal of, 2013. ISSN 2168-2194. doi: 10.1109/JBHI.2013.2294692. DOI 10.1109/JBHI.2013.2294692.en
dc.relation.hasversionIoan Stanculescu, Christopher K. I. Williams, and Yvonne Freer. A hierarchical switching linear dynamical system applied to the detection of sepsis in neonatal condition monitoring. In Uncertainty in Artificial Intelligence, pages 752–761, 2014.en
dc.relation.hasversionChristopher K. I. Williams and Ioan Stanculescu. Automating the Calibration of a Neonatal Condition Monitoring System. In M. Peleg, N. Lavrac, and C. Combi, editors, Proc AIME 2011, volume 6747 of LNAI, pages 240–249. Springer, 2011.en
dc.subjectvital signs monitoringen
dc.subjectmedical infomaticsen
dc.subjectneonatal sepsisen
dc.subjectprobabilistic dynamical modelsen
dc.subjectAuto-Regressive Hidden Markov Modelen
dc.subjectAR-HMMen
dc.subjectHierarchical Switching Linear Dynamical Systemen
dc.subjectHSLDSen
dc.titleDynamical models for neonatal intensive care monitoringen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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