Studying Real-World Experiences of Persons with Multiple Sclerosis during the first Covid-19 Lockdown: An Application of Natural Language Processing.

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 and von Wyl, Viktor (2022). Studying Real-World Experiences of Persons with Multiple Sclerosis during the first Covid-19 Lockdown: An Application of Natural Language Processing. JMIR medical informatics, 10(11), e37945. JMIR Publications 10.2196/37945

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BACKGROUND

The increasing availability of 'real-world data' in the form of written text hold promise for deepening our understanding of societal and health-related challenges. Textual data constitute a rich source of information which allow to capture lived experiences through a broad range of different sources of information (e.g., content, 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 form an ideal data basis for the application of natural language (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

To provide guidance for applied researcher, we developed and subsequently examined the utility and scientific value of a natural language processing (NLP) pipeline for extracting real-world experiences from textual data.

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 perspective of persons with MS. The pipeline has been implemented in Python and complemented by analyses of the 'Linguistic Inquiry and Word Count' (LIWC) software. It consists of five interconnected analysis steps: (1) Text preprocessing; (2) Sentiment analysis; (3) Descriptive text analysis; (4) Unsupervised learning - topic modelling; and (5) Results interpretation and validation.

RESULTS

A topic modelling analysis identified four distinct groups based on the topics participants were mainly concerned with: 'Contacts / communication'; 'Social environment'; 'Work'; and 'Errands / daily routines'. Notably, the sentiment analysis revealed that the 'Contacts / communication' group was characterized by a pronounced negative emotional tone underlying the text reports. This observed heterogeneity in emotional tonality underlying the reported experiences of the first Covid-19-related lockdown is likely to reflect differences in emotional burden, individual circumstances, and ways of coping with the pandemic situation, which is in line with previous research into this matter.

CONCLUSIONS

This study illustrates the timely and efficient applicability of an NLP pipeline and thereby serves as a precedent for applied researchers. Our study thereby contributes both to the dissemination of NLP techniques in the applied health sciences as well as to identifying previously unknown experiences and burdens of people with MS during the pandemic that may be relevant for future treatment.

CLINICALTRIAL

https://clinicaltrials.gov/ct2/show/NCT02980640.

Item Type:	Journal Article (Original Article)
Division/Institute:	04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology 04 Faculty of Medicine > University Psychiatric Services > University Hospital of Psychiatry and Psychotherapy 04 Faculty of Medicine > University Psychiatric Services > University Hospital of Psychiatry and Psychotherapy > Translational Research Center
UniBE Contributor:	Chan, Andrew Hao-Kuang, Kamm, Christian Philipp, Salmen, Anke, Walther, Sebastian, Hoepner, Robert
Subjects:	600 Technology > 610 Medicine & health
ISSN:	2291-9694
Publisher:	JMIR Publications
Language:	English
Submitter:	Pubmed Import
Date Deposited:	19 Oct 2022 09:08
Last Modified:	05 Dec 2022 16:26
Publisher DOI:	10.2196/37945
PubMed ID:	36252126
URI:	https://boris.unibe.ch/id/eprint/173834