Predicting Openness of Communication in Families With Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis

Publication type:
Journal article
Metadata:
Autoren
  • Vasiliki Baroutsou
  • Rodrigo Cerqueira Gonzalez Pena
  • Reka Schweighoffer
  • Maria Caiata-Zufferey
  • Sue Kim
  • Sharlene Hesse-Biber
  • Florina M Ciorba
  • Gerhard Lauer
  • Maria Katapodi
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000998464900041&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.2196/38399
eISSN
2561-326X
Externe Identifier
  • Clarivate Analytics Document Solution ID: H8NO0
  • PubMed Identifier: 36656633
Zeitschrift
JMIR FORMATIVE RESEARCH
Schlüsselwörter
  • cascade testing
  • dictionary-based approach
  • family communication
  • hereditary breast and ovarian cancer
  • HBOC
  • sentiment analysis
  • text mining
  • natural language processing
  • cancer
  • hereditary
Artikelnummer
ARTN e38399
Datum der Veröffentlichung
2023
Status
Published
Titel
Predicting Openness of Communication in Families With Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis
Sub types
  • Article
Ausgabe der Zeitschrift
7

Data source: Web of Science (Lite)

Other metadata sources:
Abstract
<jats:sec> <jats:title>Background</jats:title> <jats:p>In health care research, patient-reported opinions are a critical element of personalized medicine and contribute to optimal health care 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. Due to privacy laws, intrafamilial communication is the main avenue to inform at-risk relatives about the pathogenic variant and the possibility of increased cancer risk.</jats:p> </jats:sec> <jats:sec> <jats:title>Objective</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>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 South 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, sensitivity, specificity, and root mean square error.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>Higher “openness of communication” scores were associated with higher overall net sentiment score of the narrative, higher fear, being single, having nonacademic education, and higher informational support within the family. Our results demonstrate that NLP was highly effective in analyzing unstructured texts from individuals of different cultural and linguistic backgrounds and could also reliably predict a measure of “openness of communication” (area under the curve=0.72) in the context of genetic cancer risk associated with HBOC.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>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 expanded in the field of personalized medicine and technology-mediated communication.</jats:p> </jats:sec>
Autoren
  • Vasiliki Baroutsou
  • Rodrigo Cerqueira Gonzalez Pena
  • Reka Schweighoffer
  • Maria Caiata-Zufferey
  • Sue Kim
  • Sharlene Hesse-Biber
  • Florina M Ciorba
  • Gerhard Lauer
  • Maria Katapodi
DOI
10.2196/38399
eISSN
2561-326X
Zeitschrift
JMIR Formative Research
Sprache
en
Online publication date
2023
Paginierung
e38399 - e38399
Status
Published online
Herausgeber
JMIR Publications Inc.
Herausgeber URL
http://dx.doi.org/10.2196/38399
Datum der Datenerfassung
2023
Titel
Predicting Openness of Communication in Families With Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis
Ausgabe der Zeitschrift
7

Data source: Crossref

Abstract
<h4>Background</h4>In health care research, patient-reported opinions are a critical element of personalized medicine and contribute to optimal health care 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. Due to privacy laws, intrafamilial communication is the main avenue to inform at-risk relatives about the pathogenic variant and the possibility of increased cancer risk.<h4>Objective</h4>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.<h4>Methods</h4>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 South 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, sensitivity, specificity, and root mean square error.<h4>Results</h4>Higher "openness of communication" scores were associated with higher overall net sentiment score of the narrative, higher fear, being single, having nonacademic education, and higher informational support within the family. Our results demonstrate that NLP was highly effective in analyzing unstructured texts from individuals of different cultural and linguistic backgrounds and could also reliably predict a measure of "openness of communication" (area under the curve=0.72) in the context of genetic cancer risk associated with HBOC.<h4>Conclusions</h4>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 expanded in the field of personalized medicine and technology-mediated communication.
Addresses
  • Department of Clinical Research, University of Basel, Basel, Switzerland.
Autoren
  • Vasiliki Baroutsou
  • Rodrigo Cerqueira Gonzalez Pena
  • Reka Schweighoffer
  • Maria Caiata-Zufferey
  • Sue Kim
  • Sharlene Hesse-Biber
  • Florina M Ciorba
  • Gerhard Lauer
  • Maria Katapodi
  • CASCADE Consortium
DOI
10.2196/38399
eISSN
2561-326X
Externe Identifier
  • PubMed Identifier: 36656633
  • PubMed Central ID: PMC9896354
Open access
true
ISSN
2561-326X
Zeitschrift
JMIR formative research
Schlüsselwörter
  • CASCADE Consortium
Sprache
eng
Medium
Electronic
Online publication date
2023
Open access status
Open Access
Paginierung
e38399
Datum der Veröffentlichung
2023
Status
Published
Publisher licence
CC BY
Datum der Datenerfassung
2023
Titel
Predicting Openness of Communication in Families With Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis.
Sub types
  • research-article
  • Journal Article
Ausgabe der Zeitschrift
7

Files

https://formative.jmir.org/2023/1/e38399/PDF https://europepmc.org/articles/PMC9896354?pdf=render

Data source: Europe PubMed Central

Abstract
BACKGROUND: In health care research, patient-reported opinions are a critical element of personalized medicine and contribute to optimal health care 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. Due to privacy laws, intrafamilial communication is the main avenue to inform at-risk relatives about the pathogenic variant and the possibility of increased cancer risk. 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 South 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, sensitivity, specificity, and root mean square error. RESULTS: Higher "openness of communication" scores were associated with higher overall net sentiment score of the narrative, higher fear, being single, having nonacademic education, and higher informational support within the family. Our results demonstrate that NLP was highly effective in analyzing unstructured texts from individuals of different cultural and linguistic backgrounds and could also reliably predict a measure of "openness of communication" (area under the curve=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 expanded in the field of personalized medicine and technology-mediated communication.
Date of acceptance
2022
Autoren
  • Vasiliki Baroutsou
  • Rodrigo Cerqueira Gonzalez Pena
  • Reka Schweighoffer
  • Maria Caiata-Zufferey
  • Sue Kim
  • Sharlene Hesse-Biber
  • Florina M Ciorba
  • Gerhard Lauer
  • Maria Katapodi
  • CASCADE Consortium
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/36656633
DOI
10.2196/38399
eISSN
2561-326X
Externe Identifier
  • PubMed Central ID: PMC9896354
Zeitschrift
JMIR Form Res
Schlüsselwörter
  • HBOC
  • cancer
  • cascade testing
  • dictionary-based approach
  • family communication
  • hereditary
  • hereditary breast and ovarian cancer
  • natural language processing
  • sentiment analysis
  • text mining
Sprache
eng
Country
Canada
Paginierung
e38399
PII
v7i1e38399
Datum der Veröffentlichung
2023
Status
Published online
Titel
Predicting Openness of Communication in Families With Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis.
Sub types
  • Journal Article
Ausgabe der Zeitschrift
7

Data source: PubMed

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