Using natural language processing to facilitate the harmonisation of mental health questionnaires: a validation study using real-world data

Autor: Eoin McElroy, Thomas Wood, Raymond Bond, Maurice Mulvenna, Mark Shevlin, George B. Ploubidis, Mauricio Scopel Hoffmann, Bettina Moltrecht
Jazyk: angličtina
Rok vydání: 2024
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Zdroj: BMC Psychiatry, Vol 24, Iss 1, Pp 1-9 (2024)
Druh dokumentu: article
ISSN: 1471-244X
DOI: 10.1186/s12888-024-05954-2
Popis: Abstract Background Pooling data from different sources will advance mental health research by providing larger sample sizes and allowing cross-study comparisons; however, the heterogeneity in how variables are measured across studies poses a challenge to this process. Methods This study explored the potential of using natural language processing (NLP) to harmonise different mental health questionnaires by matching individual questions based on their semantic content. Using the Sentence-BERT model, we calculated the semantic similarity (cosine index) between 741 pairs of questions from five questionnaires. Drawing on data from a representative UK sample of adults (N = 2,058), we calculated a Spearman rank correlation for each of the same pairs of items, and then estimated the correlation between the cosine values and Spearman coefficients. We also used network analysis to explore the model’s ability to uncover structures within the data and metadata. Results We found a moderate overall correlation (r = .48, p
Databáze: Directory of Open Access Journals
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