Biomedical text mining from context
Abstract/Contents
- Abstract
- Many profound insights from biomedical research and clinical practice remain hidden within the unstructured text of scientific articles and electronic medical records. Extracting structured information from biomedical text could dramatically accelerate the pace of biomedical research, but due to the high variability of natural language, it hinges on our ability to recognize when different-looking statements are saying the same thing. Unfortunately, attempts to address this problem in the biomedical domain usually involve structured lexicons and ontologies, which are expensive and time- consuming to produce. In recent years, a subdomain of natural language processing called distributional semantics has approached normalization in a different way: by learning mathematical representations of words, phrases, and relationships based on their usage patterns in large corpora. These methods can detect that two different strings are semantically related based on how they are used in context, and require little or no human effort. This dissertation illustrates how distributional approaches can be applied to several important biomedical text mining tasks, including gene, drug and disease name normalization, ontology building, and the construction of a structured radiology lexicon from clinical notes. I describe a novel distributional algorithm (EBC) for extracting relationships among biomedical entities, such as chemicals, genes and diseases, and show how it can be applied to learn the structure of chemical-gene, chemical-disease, gene-disease, and gene-gene relationships from contextual usage patterns. Finally, I apply distributional relationship extraction to two inferential tasks: curating pharmacogenomic pathways, and uncovering the mechanisms behind drug-drug interactions.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Percha, Bethany L |
|---|---|
| Associated with | Stanford University, Program in Biomedical Informatics. |
| Primary advisor | Altman, Russ |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Owen, Art B |
| Thesis advisor | Potts, Christopher, 1977- |
| Advisor | Owen, Art B |
| Advisor | Potts, Christopher, 1977- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Bethany L. Percha. |
|---|---|
| Note | Submitted to the Program in Biomedical Informatics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Bethany Lynn Percha
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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