Deconstructing complex diseases : identification of new phenotypical sub-clusters of Type 2 diabetes using machine learning

Advances in data science and technology promise to help clinicians diagnose and treat certain conditions. But there are other complex and poorly characterized illnesses for which the drivers and dependent variables are not understood well enough to take full advantage of the copious patient data that may exist. For these diseases new techniques need to be explored to gain better understanding of the nature of the disease, its subtypes, cause, consequence, and presentation. Modern genetics have shown that these diseases often have multiple subtypes, as well as multiple phenotypes as indicated by the new laboratory data. Examples of such diseases include common and important illness such as Type 2 diabetes (T2D) - affecting approximately 30 million Americans, Crohn's Disease - 1 million USA suffers, epilepsy - 3.4 million Americans, and migraines - another 3.2 million in the United States.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering and Management Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Mehta, Priyasha.

Advisor dc:contributor.advisor

• Steven J. Spear.

• Engineering and Management Program.
• System Design and Management Program.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122436

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122436