A system approach to augment clinical decision-making using machine learning

This thesis helps find limits within which automated methods can support and surpass the capabilities of medical professionals and the limits beyond which these methods are not yet adequate. This will inform later exploration about (a) what improvements in data collection, interpretation, and visualization will enhance technology's capacity and (b) what changes clinicians can make to improve their decision making-augmented or not. This thesis includes (a) describing clinical decisions, informed by literature and clinical case studies and (b) reviewing current capabilities of machine methods. This led to (c) a test experiment-how to use data about a particular condition (e.g. in-hospital mortality rate prediction) from a particular source (the MIMIC III data base). The results will help define current limits on augmenting clinical decisions and establish direction for future work including more demanding experiments.

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

• Xu, Hua,S. M.Massachusetts Institute of Technology.

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/1721.1/121803

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/121803