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

Summary

Researchers developed a specialized sentiment analysis tool to identify emotional trends within the clinical records of veterans diagnosed with lung cancer. This refined approach allows clinicians to detect meaningful signals in patient notes that correlate with objective medical data, potentially improving the monitoring of patient experiences.

Key result

Re-calibration of the labMT sentiment dictionary across 3.5 million clinical notes from 10,000 patients with lung cancer produced a sentiment signal that correlates with platelet counts and treatment data.

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 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.

Key figure