Result: The Real-World Experiences of Persons With Multiple Sclerosis During the First COVID-19 Lockdown: Application of Natural Language Processing

Title:
The Real-World Experiences of Persons With Multiple Sclerosis During the First COVID-19 Lockdown: Application of Natural Language Processing
Authors:
Chiavi, Deborah; https://orcid.org/0000-0003-0595-2054, Haag, Christina; https://orcid.org/0000-0002-9662-5245, Chan, Andrew; https://orcid.org/0000-0003-3434-7283, Kamm, Christian Philipp; https://orcid.org/0000-0002-3906-0161, Sieber, Chloé; https://orcid.org/0000-0002-6642-5082, Stanikić, Mina; https://orcid.org/0000-0002-6477-7164, Rodgers, Stephanie; https://orcid.org/0000-0003-2811-7991, Pot, Caroline; https://orcid.org/0000-0002-1146-3129, Kesselring, Jürg; https://orcid.org/0000-0002-8645-7364, Salmen, Anke; https://orcid.org/0000-0002-4751-299X, Rapold, Irene; https://orcid.org/0000-0001-5587-7510, Calabrese, Pasquale; https://orcid.org/0000-0003-3347-5187, Manjaly, Zina-Mary; https://orcid.org/0000-0002-9217-1688, Gobbi, Claudio; https://orcid.org/0000-0002-7554-0664, Zecca, Chiara; https://orcid.org/0000-0002-9990-3431, Walther, Sebastian; https://orcid.org/0000-0003-4026-3561, Stegmayer, Katharina; https://orcid.org/0000-0002-4611-8966, Hoepner, Robert; https://orcid.org/0000-0002-0115-7021, Puhan, Milo; https://orcid.org/0000-0003-4721-1879, von Wyl, Viktor; https://orcid.org/0000-0002-8754-9797
Source:
Chiavi, Deborah; Haag, Christina; Chan, Andrew; Kamm, Christian Philipp; Sieber, Chloé; Stanikić, Mina; Rodgers, Stephanie; Pot, Caroline; Kesselring, Jürg; Salmen, Anke; Rapold, Irene; Calabrese, Pasquale; Manjaly, Zina-Mary; Gobbi, Claudio; Zecca, Chiara; Walther, Sebastian; Stegmayer, Katharina; Hoepner, Robert; Puhan, Milo; von Wyl, Viktor (2022). The Real-World Experiences of Persons With Multiple Sclerosis During the First COVID-19 Lockdown: Application of Natural Language Processing. JMIR Medical Informatics, 10(11):e37945.
Publisher Information:
JMIR Publications 2022-11-10
Document Type:
Electronic Resource Electronic Resource
Index Terms:
Epidemiology, Biostatistics and Prevention Institute (EBPI), Digital Society Initiative, Institute of Implementation Science in Health Care, 610 Medicine & health, Journal Article, PeerReviewed, info:eu-repo/semantics/article, info:eu-repo/semantics/publishedVersion
URL:
https://www.zora.uzh.ch/id/eprint/226926/
https://www.zora.uzh.ch/id/eprint/226926
Availability:
Open access content. Open access content
info:eu-repo/semantics/openAccess
Creative Commons: Attribution 4.0 International (CC BY 4.0)
http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
Note:
application/pdf
info:doi/10.5167/uzh-226926
English
Other Numbers:
CHUZH oai:www.zora.uzh.ch:226926
https://www.zora.uzh.ch/id/eprint/226926/1/medinform_v10i11e37945_app1_realworldexperiences_chiavi.pdf
info:doi/10.5167/uzh-226926
info:doi/10.2196/37945
info:pmid/36252126
urn:issn:2291-9694
1443049760
Contributing Source:
HAUPTBIBLIOTHEK UNIV OF ZURICH
From OAIster®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1443049760
Database:
OAIster

Further Information

BACKGROUND The increasing availability of "real-world" data in the form of written text holds promise for deepening our understanding of societal and health-related challenges. Textual data constitute a rich source of information, allowing the capture of lived experiences through a broad range of different sources of information (eg, content and emotional tone). Interviews are the "gold standard" for gaining qualitative insights into individual experiences and perspectives. However, conducting interviews on a large scale is not always feasible, and standardized quantitative assessment suitable for large-scale application may miss important information. Surveys that include open-text assessments can combine the advantages of both methods and are well suited for the application of natural language processing (NLP) methods. While innovations in NLP have made large-scale text analysis more accessible, the analysis of real-world textual data is still complex and requires several consecutive steps. OBJECTIVE We developed and subsequently examined the utility and scientific value of an NLP pipeline for extracting real-world experiences from textual data to provide guidance for applied researchers. METHODS We applied the NLP pipeline to large-scale textual data collected by the Swiss Multiple Sclerosis (MS) registry. Such textual data constitute an ideal use case for the study of real-world text data. Specifically, we examined 639 text reports on the experienced impact of the first COVID-19 lockdown from the perspectives of persons with MS. The pipeline has been implemented in Python and complemented by analyses of the "Linguistic Inquiry and Word Count" software. It consists of the following 5 interconnected analysis steps: (1) text preprocessing; (2) sentiment analysis; (3) descriptive text analysis; (4) unsupervised learning-topic modeling; and (5) results interpretation and validation. RESULTS A topic modeling analysis identified the following 4 distinct groups based on