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

by Danne C. Elbers, Jennifer La, Joshua R. Minot, Robert Gramling, Mary T. Brophy, Nhan V. Do, Nathanael R. Fillmore, Peter S. Dodds, Christopher M. Danforth

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 at the Department of Veterans Affairs. The sentiment score of notes was calculated for two years after date of diagnosis and evaluated against a lab test (platelet count) and a combination of data points (treatments). We found that the oncology specific labMT dictionary, after re-calibration for the clinical oncology domain, produces a promising signal in notes that can be detected based on a comparative analysis to the aforementioned parameters.