Abstract
Diseases are often umbrella terms for many subcategories of disease. The identification of these subcategories is vital if we are to develop personalised treatments that are better focussed on individual patients. In this short paper, we explore the use of a combination of unsupervised learning to identify potential subclasses, and supervised learning to build models for better predicting a number of different health outcomes for patients that suffer from systemic sclerosis, a rare chronic connective tissue disorder - but one that shares many characteristics with other diseases. We explore a number of different algorithms for constructing models that simultaneously predict health outcomes and identify subcategories.
Original language | English |
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Title of host publication | Proceedings - IEEE 29th International Symposium on Computer-Based Medical Systems, CBMS 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 225-226 |
Number of pages | 2 |
Volume | 2016-August |
ISBN (Electronic) | 9781467390361 |
DOIs | |
Publication status | Published - Aug 16 2016 |
Event | 29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016 - Belfast, Ireland Duration: Jun 20 2016 → Jun 23 2016 |
Other
Other | 29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016 |
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Country/Territory | Ireland |
City | Belfast |
Period | 6/20/16 → 6/23/16 |
Keywords
- Classification
- Disease subclass
- Systemic sclerosis
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Computer Science Applications