Web-based and machine learning approaches for identification of patient-reported outcomes in inflammatory bowel disease.
Inflammatory bowel disease
Machine learning
Patient-reported outcomes
World wide web-based approach
Journal
Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
ISSN: 1878-3562
Titre abrégé: Dig Liver Dis
Pays: Netherlands
ID NLM: 100958385
Informations de publication
Date de publication:
Apr 2022
Apr 2022
Historique:
received:
14
05
2021
revised:
03
08
2021
accepted:
08
09
2021
pubmed:
1
10
2021
medline:
6
5
2022
entrez:
30
9
2021
Statut:
ppublish
Résumé
Messages from an Internet forum are raw material that emerges in a natural setting (i.e., non-induced by a research situation). The FLARE-IBD project aimed at using an innovative approach consisting of collecting messages posted by patients in an Internet forum and conducting a machine-learning study (data analysis/language processing) for developing a patient-reported outcome measuring flare in inflammatory bowel disease meeting international requirements. We used web-based and machine learning approaches, in the following steps. 1) Web-scraping to collect all available posts in an Internet forum (23 656 messages) and extracting metadata from the forum. 2) Twenty patients were randomly assigned 50 extracted messages; participants indicated whether the message corresponded or not to the flare phenomenon (labeling). If yes, participants were asked to identify excerpts from the text they considered significant flare markers (annotation). 3) The set of annotated messages underwent a vocabulary analysis. The phenomenon of flare was circumscribed with the identification of 20 surrogate flare markers classified into five dimensions with their frequency within extracted labeled data: impact on life, symptoms, extra-intestinal manifestations, drugs and environmental factors. Web-based and machine-learning approaches met international recommendations to inform the content and structure for the development of patient-reported outcomes.
Sections du résumé
BACKGROUND
BACKGROUND
Messages from an Internet forum are raw material that emerges in a natural setting (i.e., non-induced by a research situation).
AIMS
OBJECTIVE
The FLARE-IBD project aimed at using an innovative approach consisting of collecting messages posted by patients in an Internet forum and conducting a machine-learning study (data analysis/language processing) for developing a patient-reported outcome measuring flare in inflammatory bowel disease meeting international requirements.
METHODS
METHODS
We used web-based and machine learning approaches, in the following steps. 1) Web-scraping to collect all available posts in an Internet forum (23 656 messages) and extracting metadata from the forum. 2) Twenty patients were randomly assigned 50 extracted messages; participants indicated whether the message corresponded or not to the flare phenomenon (labeling). If yes, participants were asked to identify excerpts from the text they considered significant flare markers (annotation). 3) The set of annotated messages underwent a vocabulary analysis.
RESULTS
RESULTS
The phenomenon of flare was circumscribed with the identification of 20 surrogate flare markers classified into five dimensions with their frequency within extracted labeled data: impact on life, symptoms, extra-intestinal manifestations, drugs and environmental factors. Web-based and machine-learning approaches met international recommendations to inform the content and structure for the development of patient-reported outcomes.
Identifiants
pubmed: 34588153
pii: S1590-8658(21)00774-X
doi: 10.1016/j.dld.2021.09.005
pii:
doi:
Types de publication
Journal Article
Randomized Controlled Trial
Langues
eng
Sous-ensembles de citation
IM
Pagination
483-489Informations de copyright
Copyright © 2021. Published by Elsevier Ltd.
Déclaration de conflit d'intérêts
Declaration of Competing Interest None declared