Machine learning analytics for data-driven decision support in healthcare
Abstract/Contents
- Abstract
- Machine learning has the potential to revolutionize the field of healthcare. With the increasing availability of electronic healthcare data, machine learning algorithms and techniques are able to offer novel data-driven insights in the form of descriptive, predictive, and prescriptive analytics. Research efforts in machine learning-driven clinical decision support systems have demonstrated performance comparable to, or surpassing, that of doctors across a wide range of disciplines. However, very few of these solutions are implemented and used. This may be due to the solution being too specialized, too difficult to operationalize, or both. My research in machine learning for clinical decision support has focused on delivering broadly applicable and clinically actionable predictions for heart disease and opioid use and misuse. As some of the leading causes of death in the US and worldwide, these are important public health concerns. A less-explored facet of decision support in healthcare lies on operational delivery of care: improving hospital efficiency, modeling patient admissions and discharges, and preventing medical errors. While these research topics are not as popular as their clinical counterparts, the potential for real-world improvement through the study of these issues is far greater in the near-term. In this dissertation, I present novel contributions spanning both the clinical and operational delivery of care. I focus on four lines of data-driven research which have the potential to deliver widespread impact: heart disease prediction, opioid use prediction in pediatric patients, medical error reduction, and hospital discharge planning and resource allocation.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ward, Andrew Thomas |
|---|---|
| Degree supervisor | Bambos, Nicholas |
| Thesis advisor | Bambos, Nicholas |
| Thesis advisor | Duchi, John |
| Thesis advisor | Poon, Ada Shuk Yan |
| Degree committee member | Duchi, John |
| Degree committee member | Poon, Ada Shuk Yan |
| Associated with | Stanford University, Department of Electrical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Andrew Thomas Ward. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2020. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Andrew Thomas Ward
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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