Type
Text
Type
Dissertation
Advisor
Choi, Yejin | Ramakrishnan, I. V. | Fodor, Paul | Balasubramanian, Niranjan | Davulcu, Hasan.
Date
2015-12-01
Keywords
information extraction, natural language processing | Computer science
Department
Department of Computer Science.
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/77269
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
A wealth of knowledge in the biomedical domain is available in unstructured or semi-structured data repositories as natural language narratives. Much of this knowledge can provide immediate and tangible benefits in patient welfare and the healthcare industry. Extracting relevant knowledge from these natural language sources and providing them as structured information suitable for immediate real-time consumption in clinical settings is, however, a manual process restricted to human domain experts. As a result, it is expensive and time-consuming. A very real consequence of this is that the journey made by medical " knowledge nuggets" from research publications to patient care settings like hospitals often take several years. Even so, the knowledge still gets presented to clinicians in natural language -- unsuitable for machine consumption, and an impediment to the pace of work often demanded of clinicians (e.g. | in emergency rooms). Automatic extraction of this knowledge is a challenging task. Biomedical research literature is replete with language constructs that are highly specific to not just the domain, but internal sub-domains. The linguistic semantics used in discussions of, say, diabetes, are very different from the semantics used to discuss diseases like malaria that are caused by external agents. Moreover, being research literature, authors typically write for readers with a fair amount of encyclopaedic domain knowledge. Consequently, important information can often only be gleaned by identifying causal relations that are implicit. Standard information extraction methods that depend on identifying causality in text usually require explicit discourse connectives like " because" , " since" , etc. Additionally, they manage to extract only those relations that are expressed within the span of a single sentence. This proposal presents a novel relation learning methodology for biomedical natural language that is able to infer relations where (a) the relation is implicit, and (b) the related entities do not co-occur within the span of a single sentence. We show that our technique outperforms a sentence-level supervised classification approach. Further, as a human-in-the-loop (HITL) model, it is capable of augmenting biomedical knowledge bases quickly and accurately. Finally, we contribute two novel applications that demonstrate the use of such relational knowledge in providing real-time clinical decision support. | 75 pages
Recommended Citation
Banerjee, Ritwik, "Knowledge Extraction from Diverse Biomedical Corpora with Applications in Healthcare: Bridging the Translational Gap" (2015). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 3090.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/3090