Cargando…
A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation
BACKGROUND: Monitoring social media has been shown to be a useful means to capture patients’ opinions and feelings about medical issues, ranging from diseases to treatments. Health-related quality of life (HRQoL) is a useful indicator of overall patients’ health, which can be captured online. OBJECT...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838601/ https://www.ncbi.nlm.nih.gov/pubmed/35089152 http://dx.doi.org/10.2196/31528 |
_version_ | 1784650166907699200 |
---|---|
author | Renner, Simon Marty, Tom Khadhar, Mickaïl Foulquié, Pierre Voillot, Paméla Mebarki, Adel Montagni, Ilaria Texier, Nathalie Schück, Stéphane |
author_facet | Renner, Simon Marty, Tom Khadhar, Mickaïl Foulquié, Pierre Voillot, Paméla Mebarki, Adel Montagni, Ilaria Texier, Nathalie Schück, Stéphane |
author_sort | Renner, Simon |
collection | PubMed |
description | BACKGROUND: Monitoring social media has been shown to be a useful means to capture patients’ opinions and feelings about medical issues, ranging from diseases to treatments. Health-related quality of life (HRQoL) is a useful indicator of overall patients’ health, which can be captured online. OBJECTIVE: This study aimed to describe a social media listening algorithm able to detect the impact of diseases or treatments on specific dimensions of HRQoL based on posts written by patients in social media and forums. METHODS: Using a web crawler, 19 forums in France were harvested, and messages related to patients’ experience with disease or treatment were specifically collected. The SF-36 (Short Form Health Survey) and EQ-5D (Euro Quality of Life 5 Dimensions) HRQoL surveys were mixed and adapted for a tailored social media listening system. This was carried out to better capture the variety of expression on social media, resulting in 5 dimensions of the HRQoL, which are physical, psychological, activity-based, social, and financial. Models were trained using cross-validation and hyperparameter optimization. Oversampling was used to increase the infrequent dimension: after annotation, SMOTE (synthetic minority oversampling technique) was used to balance the proportions of the dimensions among messages. RESULTS: The training set was composed of 1399 messages, randomly taken from a batch of 20,000 health-related messages coming from forums. The algorithm was able to detect a general impact on HRQoL (sensitivity of 0.83 and specificity of 0.74), a physical impact (0.67 and 0.76), a psychic impact (0.82 and 0.60), an activity-related impact (0.73 and 0.78), a relational impact (0.73 and 0.70), and a financial impact (0.79 and 0.74). CONCLUSIONS: The development of an innovative method to extract health data from social media as real time assessment of patients’ HRQoL is useful to a patient-centered medical care. As a source of real-world data, social media provide a complementary point of view to understand patients’ concerns and unmet needs, as well as shedding light on how diseases and treatments can be a burden in their daily lives. |
format | Online Article Text |
id | pubmed-8838601 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-88386012022-03-07 A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation Renner, Simon Marty, Tom Khadhar, Mickaïl Foulquié, Pierre Voillot, Paméla Mebarki, Adel Montagni, Ilaria Texier, Nathalie Schück, Stéphane J Med Internet Res Original Paper BACKGROUND: Monitoring social media has been shown to be a useful means to capture patients’ opinions and feelings about medical issues, ranging from diseases to treatments. Health-related quality of life (HRQoL) is a useful indicator of overall patients’ health, which can be captured online. OBJECTIVE: This study aimed to describe a social media listening algorithm able to detect the impact of diseases or treatments on specific dimensions of HRQoL based on posts written by patients in social media and forums. METHODS: Using a web crawler, 19 forums in France were harvested, and messages related to patients’ experience with disease or treatment were specifically collected. The SF-36 (Short Form Health Survey) and EQ-5D (Euro Quality of Life 5 Dimensions) HRQoL surveys were mixed and adapted for a tailored social media listening system. This was carried out to better capture the variety of expression on social media, resulting in 5 dimensions of the HRQoL, which are physical, psychological, activity-based, social, and financial. Models were trained using cross-validation and hyperparameter optimization. Oversampling was used to increase the infrequent dimension: after annotation, SMOTE (synthetic minority oversampling technique) was used to balance the proportions of the dimensions among messages. RESULTS: The training set was composed of 1399 messages, randomly taken from a batch of 20,000 health-related messages coming from forums. The algorithm was able to detect a general impact on HRQoL (sensitivity of 0.83 and specificity of 0.74), a physical impact (0.67 and 0.76), a psychic impact (0.82 and 0.60), an activity-related impact (0.73 and 0.78), a relational impact (0.73 and 0.70), and a financial impact (0.79 and 0.74). CONCLUSIONS: The development of an innovative method to extract health data from social media as real time assessment of patients’ HRQoL is useful to a patient-centered medical care. As a source of real-world data, social media provide a complementary point of view to understand patients’ concerns and unmet needs, as well as shedding light on how diseases and treatments can be a burden in their daily lives. JMIR Publications 2022-01-28 /pmc/articles/PMC8838601/ /pubmed/35089152 http://dx.doi.org/10.2196/31528 Text en ©Simon Renner, Tom Marty, Mickaïl Khadhar, Pierre Foulquié, Paméla Voillot, Adel Mebarki, Ilaria Montagni, Nathalie Texier, Stéphane Schück. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.01.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Renner, Simon Marty, Tom Khadhar, Mickaïl Foulquié, Pierre Voillot, Paméla Mebarki, Adel Montagni, Ilaria Texier, Nathalie Schück, Stéphane A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation |
title | A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation |
title_full | A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation |
title_fullStr | A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation |
title_full_unstemmed | A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation |
title_short | A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation |
title_sort | new method to extract health-related quality of life data from social media testimonies: algorithm development and validation |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838601/ https://www.ncbi.nlm.nih.gov/pubmed/35089152 http://dx.doi.org/10.2196/31528 |
work_keys_str_mv | AT rennersimon anewmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT martytom anewmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT khadharmickail anewmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT foulquiepierre anewmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT voillotpamela anewmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT mebarkiadel anewmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT montagniilaria anewmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT texiernathalie anewmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT schuckstephane anewmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT rennersimon newmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT martytom newmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT khadharmickail newmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT foulquiepierre newmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT voillotpamela newmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT mebarkiadel newmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT montagniilaria newmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT texiernathalie newmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation AT schuckstephane newmethodtoextracthealthrelatedqualityoflifedatafromsocialmediatestimoniesalgorithmdevelopmentandvalidation |