Spinal specific lexicon for sentiment analysis of adult spinal deformity patient interviews correlate with SRS22, SF36, and ODI scores: a pilot study of 25 patients
Description
Classic health-related quality of life (HRQOL) metrics are cumbersome, time intensive, and carry biases based on the subject’s native language, educational level, and cultural values. Natural language processing (NLP) converts text into quantitative metrics. Sentiment analysis enables subject matter experts to construct domain specific lexicons to assign a value that is either negative (-1) or positive (1) to certain words. The growth of tele-health provides opportunities to apply sentiment analysis to transcripts of adult spinal deformity patient visits to derive a novel and less biased HRQOL metric. Here, we demonstrate the feasibility of constructing a spine specific lexicon for sentiment analysis to derive a HRQOL metric for an adult spinal deformity patient from the transcript of their preoperative tele-health visit. We ask 7 open ended questions about the spinal conditions, treatment and quality of life of twenty five (25) adult patients during tele-health visits. We analyze the Pearson correlation among our sentiment analysis HRQOL metric and established HRQOL metrics (SRS22, SF36, and ODI). The results show statistically significant correlations between (0.43 – 0.74) between our sentiment analysis metric and the conventional metrics. This provides evidence that applying NLP techniques to patient transcripts can yield effective HRQOL metrics. These materials, and source code support this study.
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Steps to reproduce
The code should be placed inside an R project. The file main-for-paper.R should be run. The file nlp-api.R contains a series of functions needed to run main-for-paper.R