Combining Unsupervised and Supervised Learning for Discovering Disease Subclasses
- Resource Type
- Conference
- Authors
- Bosoni, Pietro; Tucker, Allan; Bellazzi, Riccardo; Nihtyanova, Svetlana I.; Denton, Christopher P.
- Source
- 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS) Computer-Based Medical Systems (CBMS), 2016 IEEE 29th International Symposium on. :225-226 Jun, 2016
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Diseases
Skin
Unsupervised learning
Standards
Supervised learning
Lungs
Systemic sclerosis
disease subclass
classification
- Language
- ISSN
- 2372-9198
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.