Discovering sequential patterns in a UK general practice database
The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses. These patterns may be able to predict illnesses that a patient is likely to develop, allowing the implementation of preventative actions. In...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2012
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| Online Access: | https://eprints.nottingham.ac.uk/2063/ |
| _version_ | 1848790713699926016 |
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| author | Reps, Jenna Garibaldi, Jonathan M. Aickelin, Uwe Soria, Daniele Gibson, Jack E. Hubbard, Richard B. |
| author_facet | Reps, Jenna Garibaldi, Jonathan M. Aickelin, Uwe Soria, Daniele Gibson, Jack E. Hubbard, Richard B. |
| author_sort | Reps, Jenna |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The wealth of computerised medical information becoming readily available presents the opportunity to examine
patterns of illnesses, therapies and responses. These patterns may be able to predict illnesses that a patient is likely to develop, allowing the implementation of preventative actions.
In this paper sequential rule mining is applied to a General
Practice database to find rules involving a patients age, gender and medical history. By incorporating these rules into current health-care a patient can be highlighted as susceptible to a future illness based on past or current illnesses, gender and year of birth. This knowledge has the ability to greatly improve health-care and reduce health-care costs. |
| first_indexed | 2025-11-14T18:16:59Z |
| format | Conference or Workshop Item |
| id | nottingham-2063 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:16:59Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-20632020-05-04T20:22:47Z https://eprints.nottingham.ac.uk/2063/ Discovering sequential patterns in a UK general practice database Reps, Jenna Garibaldi, Jonathan M. Aickelin, Uwe Soria, Daniele Gibson, Jack E. Hubbard, Richard B. The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses. These patterns may be able to predict illnesses that a patient is likely to develop, allowing the implementation of preventative actions. In this paper sequential rule mining is applied to a General Practice database to find rules involving a patients age, gender and medical history. By incorporating these rules into current health-care a patient can be highlighted as susceptible to a future illness based on past or current illnesses, gender and year of birth. This knowledge has the ability to greatly improve health-care and reduce health-care costs. 2012 Conference or Workshop Item PeerReviewed Reps, Jenna, Garibaldi, Jonathan M., Aickelin, Uwe, Soria, Daniele, Gibson, Jack E. and Hubbard, Richard B. (2012) Discovering sequential patterns in a UK general practice database. In: 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, 5-7 Jan 2012, Hong Kong. (In Press) |
| spellingShingle | Reps, Jenna Garibaldi, Jonathan M. Aickelin, Uwe Soria, Daniele Gibson, Jack E. Hubbard, Richard B. Discovering sequential patterns in a UK general practice database |
| title | Discovering sequential patterns in a UK general practice database |
| title_full | Discovering sequential patterns in a UK general practice database |
| title_fullStr | Discovering sequential patterns in a UK general practice database |
| title_full_unstemmed | Discovering sequential patterns in a UK general practice database |
| title_short | Discovering sequential patterns in a UK general practice database |
| title_sort | discovering sequential patterns in a uk general practice database |
| url | https://eprints.nottingham.ac.uk/2063/ |