JMIR Publications

ABSTRACT

Background:

In healthcare research, the value of patient-reported opinions is a critical element of personalized medicine and contributes to optimal healthcare delivery. The importance of integrating Natural Language Processing (NLP) methods to extract patient-reported opinions has been gradually acknowledged over the past years. One form of NLP is sentiment analysis, which extracts and analyses information by detecting feelings (thoughts, emotions, attitudes, etc) behind words. Sentiment analysis has become particularly popular following the rise of digital interactions. However, NLP and sentiment analysis in the context of intrafamilial communication for genetic cancer risk is still unexplored.

Objective:

The study examined the role of sentiment in predicting openness of intrafamilial communication about genetic cancer risk associated with Hereditary Breast and Ovarian Cancer (HBOC) syndrome.

Methods:

We used narratives derived from 53 in-depth interviews with individuals from families that harbor pathogenic variants associated with HBOC first to quantify openness of communication about cancer risk, and second to examine the role of sentiment in predicting openness of communication. The interviews were conducted between 2019 and 2021 in Switzerland and in Korea using the same interview guide. We used NLP to extract and quantify textual features to construct a handcrafted lexicon about interpersonal communication of genetic testing results and cancer risk associated with HBOC. Moreover, we examined the role of sentiment in predicting openness of communication using a stepwise linear regression model. To test model accuracy, we used a split-validation set. We measured the performance of the training and testing model using area under the curve (AUROC), sensitivity, specificity and root mean square error (RMSE).

Results:

The overall net sentiment of narratives and fear which were obtained based on the NRC Emotion Lexicon, being single, having non-academic education and informational support within the family were positively associated with “openness of communication”. Our results demonstrate that NLP was highly effective in analysing unstructured texts from individuals of different cultural and linguistic backgrounds and could also reliably predict a measure of “openness of communication” (AUROC: 0.72) in the context of genetic cancer risk associated with HBOC.

Conclusions:

Our study showed that NLP can facilitate assessment of openness of communication in individuals carrying a pathogenic variant associated with HBOC. Findings provided promising evidence that various features from narratives such as sentiment and fear are important predictors of interpersonal communication and self-disclosure in this context. Our approach is promising and can be further used and expanded in the field of personalized medicine.