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Sentiment analysis of medical notes for lung cancer patients at the Department of Veterans Affairs

Elbers, Danne C
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Abstract
Natural language processing of medical records offers tremendous potential to improve the patient experience. Sentiment analysis of clinical notes has been performed with mixed results, often highlighting the issue that dictionary ratings are not domain specific. Here, for the first time, we re-calibrate the labMT sentiment dictionary on 3.5M clinical notes describing 10,000 patients diagnosed with lung cancer. The found sentiment score of notes was evaluated against platelet counts and treatment arms. We found that the oncology specific labMT dictionary produces a promising signal in notes that can be detected based on a comparative analysis to the aforementioned parameters.
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Date
2022-01-01
Student Status
Graduate
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Type of presentation
virtual-oral-presentation
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UVM Student Research Conference
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URI
https://hdl.handle.net/20.500.14849/8320
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Advisor(s)
Chris Danforth
Peter Dodds
Department
Program/Major
Complex Systems
College/School
College of Engineering and Mathematical Sciences
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Health Sciences
Engineering & Physical Sciences
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