Adelaide Research & Scholarship
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https://hdl.handle.net/2440/134569
| Type: | Thesis |
| Title: | Predicting clinical deterioration |
| Author: | Malycha, James |
| Issue Date: | 2021 |
| School/Discipline: | Adelaide Medical School |
| Abstract: | This thesis describes the development of a prognostic algorithm that uses Electronic Patient Record (EPR) data to predict potentially avoidable adverse events (e.g., cardiac arrest/unanticipated Intensive Care Unit (ICU) admission) in sufficient time so that interventions can take place in patients admitted to the hospital ward. The system is called Hospital-wide Alerts Via Electronic Noticeboard (HAVEN). The thesis is composed of six chapters: evaluating variables for potential inclusion in HAVEN (chapter 1), evaluating the prognostic value of fractional inspired oxygen for potential inclusion in HAVEN (chapter 2), evaluating HAVEN in the ward environment (chapter 3), validating HAVEN (chapter 4), working towards improved outcome measures for HAVEN (chapter 5) and the automated quantification of the clinical workload associated with systems like HAVEN (chapter 6). |
| Advisor: | Ludbrook, Guy Watkinson, Peter Young, Duncan |
| Dissertation Note: | Thesis (Ph.D.) -- University of Adelaide, Adelaide Medical School, 2022 |
| Keywords: | Clinical deterioration electronc patient record intensive care unit machine learning rapid response system early warning score |
| Provenance: | This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals |
| Appears in Collections: | Research Theses |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Malycha2022_PhD.pdf | 3.64 MB | Adobe PDF | View/Open |
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