Comparison of natural language processing algorithms for medical texts
Author(s)Chen, Michelle W., M. Eng. Massachusetts Institute of Technology
Comparison of NLP systems for medical text
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
MetadataShow full item record
With the large corpora of clinical texts, natural language processing (NLP) is growing to be a field that people are exploring to extract useful patient information. NLP applications in clinical medicine are especially important in domains where the clinical observations are crucial to define and diagnose the disease. There are a variety of different systems that attempt to match words and word phrases to medical terminologies. Because of the differences in annotation datasets and lack of common conventions, many of the systems yield conflicting results. The purpose of this thesis project is (1) to create a visual representation of how different concepts compare to each other when using various annotators and (2) to improve upon the NLP methods to yield terms with better fidelity to what the clinicians are trying to express.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Title as it appears in MIT Commencement Exercises program, June 5, 2015: Comparison of NLP systems for medical text. Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 57-58).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.