One size fits all: Enhanced zero-shot text classification for patient listening on social media
Patient-focused drug development (PFDD) represents a transformative approach that is reshaping the pharmaceutical landscape by centering on patients throughout the drug development process. Recent advancements in Artificial Intelligence (AI), especially in Natural Language Processing (NLP), have ena...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2024.1397470/full |
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| author | Veton Matoshi Maria Carmela De Vuono Roberto Gaspari Mark Kröll Michael Jantscher Sara Lucia Nicolardi Giuseppe Mazzola Manuela Rauch Vedran Sabol Eileen Salhofer Riccardo Mariani |
| author_facet | Veton Matoshi Maria Carmela De Vuono Roberto Gaspari Mark Kröll Michael Jantscher Sara Lucia Nicolardi Giuseppe Mazzola Manuela Rauch Vedran Sabol Eileen Salhofer Riccardo Mariani |
| author_sort | Veton Matoshi |
| collection | DOAJ |
| description | Patient-focused drug development (PFDD) represents a transformative approach that is reshaping the pharmaceutical landscape by centering on patients throughout the drug development process. Recent advancements in Artificial Intelligence (AI), especially in Natural Language Processing (NLP), have enabled the analysis of vast social media datasets, also called Social Media Listening (SML), providing insights not only into patient perspectives but also into those of other interest groups such as caregivers. In this method study, we propose an NLP framework that—given a particular disease—is designed to extract pertinent information related to three primary research topics: identification of interest groups, understanding of challenges, and assessing treatments and support systems. Leveraging external resources like ontologies and employing various NLP techniques, particularly zero-shot text classification, the presented framework yields initial meaningful insights into these research topics with minimal annotation effort. |
| format | Article |
| id | doaj-art-5240787792f2405a97a505b7a97d967d |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-5240787792f2405a97a505b7a97d967d2025-02-11T06:59:09ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-02-01710.3389/frai.2024.13974701397470One size fits all: Enhanced zero-shot text classification for patient listening on social mediaVeton Matoshi0Maria Carmela De Vuono1Roberto Gaspari2Mark Kröll3Michael Jantscher4Sara Lucia Nicolardi5Giuseppe Mazzola6Manuela Rauch7Vedran Sabol8Eileen Salhofer9Riccardo Mariani10Independent Researcher, Graz, AustriaKnow Center Research GmbH, Graz, AustriaKnow Center Research GmbH, Graz, AustriaChiesi Farmaceutici S.p.A, Parma, ItalyChiesi Farmaceutici S.p.A, Parma, ItalyKnow Center Research GmbH, Graz, AustriaKnow Center Research GmbH, Graz, AustriaChiesi Farmaceutici S.p.A, Parma, ItalyChiesi Farmaceutici S.p.A, Parma, ItalyChiesi Farmaceutici S.p.A, Parma, ItalyKnow Center Research GmbH, Graz, AustriaPatient-focused drug development (PFDD) represents a transformative approach that is reshaping the pharmaceutical landscape by centering on patients throughout the drug development process. Recent advancements in Artificial Intelligence (AI), especially in Natural Language Processing (NLP), have enabled the analysis of vast social media datasets, also called Social Media Listening (SML), providing insights not only into patient perspectives but also into those of other interest groups such as caregivers. In this method study, we propose an NLP framework that—given a particular disease—is designed to extract pertinent information related to three primary research topics: identification of interest groups, understanding of challenges, and assessing treatments and support systems. Leveraging external resources like ontologies and employing various NLP techniques, particularly zero-shot text classification, the presented framework yields initial meaningful insights into these research topics with minimal annotation effort.https://www.frontiersin.org/articles/10.3389/frai.2024.1397470/fullpatient-focused drug developmentsocial media listeningpatient’s perspectivepatient centriczero-shot classificationnamed entity recognition |
| spellingShingle | Veton Matoshi Maria Carmela De Vuono Roberto Gaspari Mark Kröll Michael Jantscher Sara Lucia Nicolardi Giuseppe Mazzola Manuela Rauch Vedran Sabol Eileen Salhofer Riccardo Mariani One size fits all: Enhanced zero-shot text classification for patient listening on social media Frontiers in Artificial Intelligence patient-focused drug development social media listening patient’s perspective patient centric zero-shot classification named entity recognition |
| title | One size fits all: Enhanced zero-shot text classification for patient listening on social media |
| title_full | One size fits all: Enhanced zero-shot text classification for patient listening on social media |
| title_fullStr | One size fits all: Enhanced zero-shot text classification for patient listening on social media |
| title_full_unstemmed | One size fits all: Enhanced zero-shot text classification for patient listening on social media |
| title_short | One size fits all: Enhanced zero-shot text classification for patient listening on social media |
| title_sort | one size fits all enhanced zero shot text classification for patient listening on social media |
| topic | patient-focused drug development social media listening patient’s perspective patient centric zero-shot classification named entity recognition |
| url | https://www.frontiersin.org/articles/10.3389/frai.2024.1397470/full |
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