UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Modelling deterioration of health and predicting mortality using the vital signs of critical care patients

Pollard, T; (2017) Modelling deterioration of health and predicting mortality using the vital signs of critical care patients. Doctoral thesis , UCL (University College London).

Full text not available from this repository.

Abstract

Advanced bedside monitors are now commonplace in hospitals, presenting clinicians with increasingly large volumes of data on which to guide treatment. Despite these advances, there remains a heavy reliance on clinical intuition and simplistic alarm systems for diagnosis and care planning. Objectively assessing physiological stability using multiple, dynamic variables presents a challenge and early detection of deterioration can be problematic. This interdisciplinary thesis explores how improvements in data-archiving, networking, and sharing present new opportunities for clinical research and health care. Inspired by an initial investigation to predict mortality in patients with an artificial neural network, we move on to develop a probabilistic, data-fusion model for objectively assessing clinical deterioration and recovery. We demonstrate how this model of “normal” physiology can be used to indicate health trajectory, using novel approaches to define the normal population and to construct the model. Concluding the thesis, we highlight potential for the data-fusion model and suggest directions for future research.

Type: Thesis (Doctoral)
Title: Modelling deterioration of health and predicting mortality using the vital signs of critical care patients
Event: UCL (University College London)
Language: English
UCL classification: UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Maths and Physical Sciences
URI: http://discovery.ucl.ac.uk/id/eprint/1551609
Downloads since deposit
0Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item