Educational and Emotional Needs of Patients with Myelodysplastic Syndromes: An AI Analysis of Multi-Country Social Media
Notice bibliographique
Résumé
Myelodysplastic syndromes (MDS) comprise a heterogeneous group of myeloid malignancies characterized by high symptom burden and limited treatment options. A central challenge to caring for patients with MDS is assessing their needs throughout the different phases of the disease. Patients and caregivers frequently consult online sources to address informational and emotional support needs. We conducted a social listening analysis of publicly available online forums to identify unmet needs of patients with MDS and their caregivers in the USA, the UK, Spain, Canada, France, and China. We used artificial intelligence (AI) and natural language processing (NLP) to group categories of posts into seven overarching motivations for online engagement (Clinical, Emotional, Treatments, Transplant, Education and Logistics, Physical, and Diet and Lifestyle). Posts from the USA and China commonly discussed clinical topics such as MDS diagnosis, disease monitoring, and progression. Posts from Canada and France were frequently about treatments and treatment options. Emotional concerns were key drivers of posts from Canada, Spain, and the UK. Additionally, we also identified topics associated with negative language at key phases during the treatment experience where patients and caregivers exhibited increased online engagement, revealing educational and emotional support gaps at the time of diagnosis, when patients are deciding between treatment options, and when treatment options fail. In this research, based on social media listening analyzed using AI and NLP, potential information gaps and unmet needs among patients with MDS were identified. Addressing these gaps through targeted patient education and guidance to emotional support options during these phases could reduce the disease burden and emotional distress experienced by patients with MDS. To better understand patients’ needs, we conducted a social listening analysis of frequently discussed topics on public online MDS forums in the USA, UK, Spain, Canada, France, and China. We used artificial intelligence and natural language processing to group motivations for online engagement into seven themes: Clinical, Emotional, Treatments, Transplant, Education/Logistics, Physical, Diet/Lifestyle. Forum posts from the USA and China usually discussed clinical topics like diagnosis, disease monitoring, and disease progression. Posts about treatments and exploring treatment options were a priority in Canada and France. Emotional concerns were a key focus among posts from Canada, France, Spain, and the UK. We also identified topics that contained strong negative sentiment at key milestones where patients/caregivers had increased online engagement. This revealed that educational/emotional support was most insufficient at the time of diagnosis, when patients are deciding between treatments, and when treatments fail. Addressing these concerns through improving patient education and offering guidance to emotional support options during specific phases of the disease journey could help manage the impact of patients’ symptoms, improve their disease and treatment experience, and thus potentially enhance the quality-of-life of patients with MDS.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».