Sentiment analysis of medical notes for lung cancer patients at the Department of Veterans Affairs
Conference Year
January 2022
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.
Primary Faculty Mentor Name
Chris Danforth
Secondary Mentor Name
Peter Dodds
Faculty/Staff Collaborators
UVM: Peter Dodds, Chris Danforth, Robert Gramling, VA (non-UVM): Jennifer La, Mary Brophy, Nhan Do, Nathanael Fillmore
Student Collaborators
Josh Minot
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Complex Systems
Primary Research Category
Health Sciences
Secondary Research Category
Engineering & Physical Sciences
Sentiment analysis of medical notes for lung cancer patients at the Department of Veterans Affairs
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.