Personalised patient monitoring in haemodialysis using hierarchical Gaussian processes
The prevalence of end-stage renal failure is 861 per million population in the UK, and these patients undergo three haemodialysis sessions per week, each lasting 4 hours. In addition, patients are at risk of intra-dialytic hypotension, which leads to chronic heart disease and a high incidence of mor...
| Príomhchruthaitheoirí: | , , , |
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| Formáid: | Conference item |
| Foilsithe / Cruthaithe: |
Institute of Electrical and Electronics Engineers
2017
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| _version_ | 1826306085248565248 |
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| author | Zhu, T Colopy, G Pugh, C Clifton, D |
| author_facet | Zhu, T Colopy, G Pugh, C Clifton, D |
| author_sort | Zhu, T |
| collection | OXFORD |
| description | The prevalence of end-stage renal failure is 861 per million population in the UK, and these patients undergo three haemodialysis sessions per week, each lasting 4 hours. In addition, patients are at risk of intra-dialytic hypotension, which leads to chronic heart disease and a high incidence of mortality. Through continuous monitoring of blood pressure during dialysis, we describe the use of Gaussian process regression to model changes of systolic blood pressure over time for each patient. We use Hierarchical Gaussian processes to infer the assumed latent structure of the systolic blood pressure trajectory for each individual patient, to describe their personalised “normal” and “abnormal” physiological patterns. |
| first_indexed | 2024-03-07T06:42:35Z |
| format | Conference item |
| id | oxford-uuid:f9cc27d2-d47a-45f5-8f7a-a3a8f792b303 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T06:42:35Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | oxford-uuid:f9cc27d2-d47a-45f5-8f7a-a3a8f792b3032022-03-27T13:00:38ZPersonalised patient monitoring in haemodialysis using hierarchical Gaussian processesConference itemhttp://purl.org/coar/resource_type/c_5794uuid:f9cc27d2-d47a-45f5-8f7a-a3a8f792b303Symplectic Elements at OxfordInstitute of Electrical and Electronics Engineers2017Zhu, TColopy, GPugh, CClifton, DThe prevalence of end-stage renal failure is 861 per million population in the UK, and these patients undergo three haemodialysis sessions per week, each lasting 4 hours. In addition, patients are at risk of intra-dialytic hypotension, which leads to chronic heart disease and a high incidence of mortality. Through continuous monitoring of blood pressure during dialysis, we describe the use of Gaussian process regression to model changes of systolic blood pressure over time for each patient. We use Hierarchical Gaussian processes to infer the assumed latent structure of the systolic blood pressure trajectory for each individual patient, to describe their personalised “normal” and “abnormal” physiological patterns. |
| spellingShingle | Zhu, T Colopy, G Pugh, C Clifton, D Personalised patient monitoring in haemodialysis using hierarchical Gaussian processes |
| title | Personalised patient monitoring in haemodialysis using hierarchical Gaussian processes |
| title_full | Personalised patient monitoring in haemodialysis using hierarchical Gaussian processes |
| title_fullStr | Personalised patient monitoring in haemodialysis using hierarchical Gaussian processes |
| title_full_unstemmed | Personalised patient monitoring in haemodialysis using hierarchical Gaussian processes |
| title_short | Personalised patient monitoring in haemodialysis using hierarchical Gaussian processes |
| title_sort | personalised patient monitoring in haemodialysis using hierarchical gaussian processes |
| work_keys_str_mv | AT zhut personalisedpatientmonitoringinhaemodialysisusinghierarchicalgaussianprocesses AT colopyg personalisedpatientmonitoringinhaemodialysisusinghierarchicalgaussianprocesses AT pughc personalisedpatientmonitoringinhaemodialysisusinghierarchicalgaussianprocesses AT cliftond personalisedpatientmonitoringinhaemodialysisusinghierarchicalgaussianprocesses |