Natural language processing for precision clinical diagnostics and treatment
Name
1078691724-MIT.pdf
Description
Full printable version
Size
2.18 MB
Format
Adobe PDF
Checksum (MD5)
ff6b1edf4e10f476c4859c05c17271fd
Author(s)
Chien, Isabel
Advisor(s)
Manolis Kellis.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, I focus upon application of natural language processing to clinical diagnostics and treatment within the palliative care and serious illness field. I explore a variety of natural language processing methods, including deep learning, rule-based, and classic machine learning, and applied to the identication of documentation reflecting advanced care planning measures, serious illnesses, and serious illness symptoms. I introduce two tools that can be used to analyze clinical notes from electronic health records: ClinicalRegex, a regular expression interface, and PyCCI, an a clinical text annotation tool. Additionally, I discuss a palliative care-focused research project in which I apply machine learning natural language processing methods to identifying clinical documentation in the palliative care and serious illness field. Advance care planning, which includes clarifying and documenting goals of care and preferences for future care, is essential for achieving end-of-life care that is consistent with the preferences of dying patients and their families. Physicians document their communication about these preferences as unstructured free text in clinical notes; as a result, routine assessment of this quality indicator is time consuming and costly. Integrating goals of care conversations and advance care planning into decision-making about palliative surgery have been shown to result in less invasive care near the time of death and improve clinical outcomes for both the patient and surviving family members. Natural language processing methods offer an efficient and scalable way to improve the visibility of documented serious illness conversations within electronic health record data, helping to better quality of care.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 61-65).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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