Combining unsupervised and supervised learning for discovering disease subclasses

Pietro Bosoni, Allan Tucker, Riccardo Bellazzi, Svetlana I. Nihtyanova, Christopher P. Denton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings - IEEE 29th International Symposium on Computer-Based Medical Systems, CBMS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages225-226
Number of pages2
Volume2016-August
ISBN (Electronic)9781467390361
DOIs
Publication statusPublished - Aug 16 2016
Event29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016 - Belfast, Ireland
Duration: Jun 20 2016Jun 23 2016

Other

Other29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016
CountryIreland
CityBelfast
Period6/20/166/23/16

Keywords

  • Classification
  • Disease subclass
  • Systemic sclerosis

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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