AutomedicationFrancoV1, Main, Exploration, bibRecord, 000378

Understanding the patient perspective of epilepsy treatment through text mining of online patient support groups.

Identifieur interne : 000378 ( Main/Exploration ); précédent : 000377; suivant : 000379

Understanding the patient perspective of epilepsy treatment through text mining of online patient support groups.

Auteurs : Kai He [République populaire de Chine] ; Na Hong [États-Unis] ; Samuel Lapalme-Remis [Canada] ; Yangyang Lan [République populaire de Chine] ; Ming Huang [États-Unis] ; Chen Li [République populaire de Chine] ; Lixia Yao [États-Unis]

Source :

Epilepsy & behavior : E&B [ 1525-5069 ] ; 2019.

RBID : pubmed:30893617

Descripteurs français

KwdFr :
- Adaptation psychologique (physiologie), Fouille de données (méthodes), Groupes d'entraide (MeSH), Humains (MeSH), Maladie chronique (psychologie), Maladie chronique (thérapie), Médias sociaux (MeSH), Participation des patients (MeSH), Réseautage social (MeSH), Épilepsie (psychologie), Épilepsie (thérapie).
MESH :
- méthodes : Fouille de données.
- physiologie : Adaptation psychologique.
- psychologie : Maladie chronique, Épilepsie.
- thérapie : Maladie chronique, Épilepsie.
- Groupes d'entraide, Humains, Médias sociaux, Participation des patients, Réseautage social.

English descriptors

KwdEn :
- Adaptation, Psychological (physiology), Chronic Disease (psychology), Chronic Disease (therapy), Data Mining (methods), Epilepsy (psychology), Epilepsy (therapy), Humans (MeSH), Patient Participation (MeSH), Self-Help Groups (MeSH), Social Media (MeSH), Social Networking (MeSH).
MESH :
- methods : Data Mining.
- physiology : Adaptation, Psychological.
- psychology : Chronic Disease, Epilepsy.
- therapy : Chronic Disease, Epilepsy.
- Humans, Patient Participation, Self-Help Groups, Social Media, Social Networking.

Abstract

OBJECTIVE

Epilepsy is among the most common chronic neurologic diseases. There is a need for more data on patient perspectives of treatment to guide patient-centered care initiatives. Patients with epilepsy share their experiences on social media anonymously, but little is known about those discussions. Our aim was to learn what patients with epilepsy discuss regarding their condition and identify treatment-related themes from online patient support groups.

METHODS

A total of 355,838 posts were collected from three online support groups for patients with epilepsy through a crawling script, and an analytical pipeline was built to identify patient conversation content through leveraging of multiple text mining methods. Results were also displayed by network visualization methods.

RESULTS

Patients with epilepsy sought information about medical treatments, shared their treatment experiences, and sought help from other posters. Key themes related to treatments included the search for optimal personalized treatment strategies as well as identifying and coping with adverse effects.

SIGNIFICANCE

This study showed the feasibility of learning about concerns of patients with epilepsy, especially treatment issues, through text mining methods. However, some manual selection and filtering were necessary to ensure quality results for the treatment analysis. Providers should be aware of online discussions and use analyses of such discussions to help guide effective patient engagement during care.

DOI: 10.1016/j.yebeh.2019.02.002
PubMed: 30893617

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 000549
to stream Main, to step Curation: 000549

Le document en format XML

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<term>Epilepsy (therapy)</term>
<term>Humans (MeSH)</term>
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<term>Maladie chronique (thérapie)</term>
<term>Médias sociaux (MeSH)</term>
<term>Participation des patients (MeSH)</term>
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<front><div type="abstract" xml:lang="en"><p><b>OBJECTIVE</b>
</p>
<p>Epilepsy is among the most common chronic neurologic diseases. There is a need for more data on patient perspectives of treatment to guide patient-centered care initiatives. Patients with epilepsy share their experiences on social media anonymously, but little is known about those discussions. Our aim was to learn what patients with epilepsy discuss regarding their condition and identify treatment-related themes from online patient support groups.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>METHODS</b>
</p>
<p>A total of 355,838 posts were collected from three online support groups for patients with epilepsy through a crawling script, and an analytical pipeline was built to identify patient conversation content through leveraging of multiple text mining methods. Results were also displayed by network visualization methods.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>RESULTS</b>
</p>
<p>Patients with epilepsy sought information about medical treatments, shared their treatment experiences, and sought help from other posters. Key themes related to treatments included the search for optimal personalized treatment strategies as well as identifying and coping with adverse effects.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>SIGNIFICANCE</b>
</p>
<p>This study showed the feasibility of learning about concerns of patients with epilepsy, especially treatment issues, through text mining methods. However, some manual selection and filtering were necessary to ensure quality results for the treatment analysis. Providers should be aware of online discussions and use analyses of such discussions to help guide effective patient engagement during care.</p>
</div>
</front>
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<Abstract><AbstractText Label="OBJECTIVE">Epilepsy is among the most common chronic neurologic diseases. There is a need for more data on patient perspectives of treatment to guide patient-centered care initiatives. Patients with epilepsy share their experiences on social media anonymously, but little is known about those discussions. Our aim was to learn what patients with epilepsy discuss regarding their condition and identify treatment-related themes from online patient support groups.</AbstractText>
<AbstractText Label="METHODS">A total of 355,838 posts were collected from three online support groups for patients with epilepsy through a crawling script, and an analytical pipeline was built to identify patient conversation content through leveraging of multiple text mining methods. Results were also displayed by network visualization methods.</AbstractText>
<AbstractText Label="RESULTS">Patients with epilepsy sought information about medical treatments, shared their treatment experiences, and sought help from other posters. Key themes related to treatments included the search for optimal personalized treatment strategies as well as identifying and coping with adverse effects.</AbstractText>
<AbstractText Label="SIGNIFICANCE">This study showed the feasibility of learning about concerns of patients with epilepsy, especially treatment issues, through text mining methods. However, some manual selection and filtering were necessary to ensure quality results for the treatment analysis. Providers should be aware of online discussions and use analyses of such discussions to help guide effective patient engagement during care.</AbstractText>
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<country name="États-Unis"><region name="Minnesota"><name sortKey="Hong, Na" sort="Hong, Na" uniqKey="Hong N" first="Na" last="Hong">Na Hong</name>
</region>
<name sortKey="Huang, Ming" sort="Huang, Ming" uniqKey="Huang M" first="Ming" last="Huang">Ming Huang</name>
<name sortKey="Yao, Lixia" sort="Yao, Lixia" uniqKey="Yao L" first="Lixia" last="Yao">Lixia Yao</name>
</country>
<country name="Canada"><noRegion><name sortKey="Lapalme Remis, Samuel" sort="Lapalme Remis, Samuel" uniqKey="Lapalme Remis S" first="Samuel" last="Lapalme-Remis">Samuel Lapalme-Remis</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Sante/explor/AutomedicationFrancoV1/Data/Main/Exploration

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000378 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000378 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Sante
   |area=    AutomedicationFrancoV1
   |flux=    Main
   |étape=   Exploration
   |type=    RBID
   |clé=     pubmed:30893617
   |texte=   Understanding the patient perspective of epilepsy treatment through text mining of online patient support groups.
}}

Pour générer des pages wiki

HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i   -Sk "pubmed:30893617" \
       | HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd   \
       | NlmPubMed2Wicri -a AutomedicationFrancoV1

This area was generated with Dilib version V0.6.38.
Data generation: Mon Mar 15 15:24:36 2021. Site generation: Mon Mar 15 15:32:03 2021

	Serveur d'exploration sur l'automédication dans le monde francophone
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.

Serveur d'exploration sur l'automédication dans le monde francophone

Understanding the patient perspective of epilepsy treatment through text mining of online patient support groups.

Understanding the patient perspective of epilepsy treatment through text mining of online patient support groups.

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki