Evaluation of language analysis to summarize the literature: a comparison to traditional meta-analysis in primary hip and knee surgery

Abstract

IntroductionSentiment analysis, by evaluating written wording and its context, is a growing tool used in computer science that can determine the level of support expressed in a body of text using artificial intelligence methodologies. The application of sentiment analysis to biomedical literature is a growing field and offers the potential to rapidly and economically explore large amounts of published research and characterize treatment efficacy.MethodsWe compared the results of sentiment analysis of 115 article abstracts analyzed in a recently published meta-analysis of peripheral nerve block usage in primary hip and knee arthroplasty to the conclusions drawn by the authors of the original meta-analysis.ResultsA moderately positive outlook supporting the utilization of regional anesthesia for hip and knee arthroplasty was found in the 115 articles that were included for analysis, with 46% expressing positive sentiment, 35% expressing neutral sentiment, and 19% of abstracts expressing negative sentiment. This was well aligned with the conclusions reached by a previous meta-analysis of the same articles.DiscussionSentiment analysis applied to the medical literature can rapidly evaluate large collections of published data and generate an impression of overall findings that are aligned with the findings of a traditional meta-analysis.

Comparison of computer-generated sentiment analysis to traditional meta-analysis: an infographic

“…Recently, it has been applied to biomedical literature. The purpose of this study by Myszewski et al 1 was to compare the results of sentiment analysis of 115 article abstracts from a recent meta-analysis of peripheral block usage in primary hip and knee arthroplasty to the conclusions published by the meta-analysis. A modified GAN-BioBERT (Bidirectional Encoder Representations from Transformers) algorithm model was used for this analysis.…”