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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...

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Autores principales: Renner, Simon, Marty, Tom, Khadhar, Mickaïl, Foulquié, Pierre, Voillot, Paméla, Mebarki, Adel, Montagni, Ilaria, Texier, Nathalie, Schück, Stéphane
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
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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.
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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
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