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METHODS article

Front. Artif. Intell.
Sec. Natural Language Processing
Volume 7 - 2024 | doi: 10.3389/frai.2024.1397470

One size fits all: Enhanced zero-shot text classification for Patient Listening on Social Media

Provisionally accepted
Veton Matoshi Veton Matoshi 1*Maria Carmela De Vuono Maria Carmela De Vuono 2Roberto Gaspari Roberto Gaspari 2Mark Kröll Mark Kröll 3Michael Jantscher Michael Jantscher 3Sara Lucia Nicolardi Sara Lucia Nicolardi 2Giuseppe Mazzola Giuseppe Mazzola 2Manuela Rauch Manuela Rauch 3Vedran Sabol Vedran Sabol 3Eileen Salhofer Eileen Salhofer 3Riccardo Mariani Riccardo Mariani 2
  • 1 Other, Graz, Austria
  • 2 Chiesi Farmaceutici (Italy), Parma, Emilia-Romagna, Italy
  • 3 Know Center, Graz, Styria, Austria

The final, formatted version of the article will be published soon.

    Patient-focused drug development (PFDD) represents a transformative approach that is reshaping the pharmaceutical landscape by centering on patients throughout the drug development process (Perfetto et al., 2015). 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.

    Keywords: patient-focused drug development, Social media listening, Patient's perspective, Patient centric, Zero-shot classification, named entity recognition, Relation extraction

    Received: 07 Mar 2024; Accepted: 04 Dec 2024.

    Copyright: © 2024 Matoshi, De Vuono, Gaspari, Kröll, Jantscher, Nicolardi, Mazzola, Rauch, Sabol, Salhofer and Mariani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Veton Matoshi, Other, Graz, Austria

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.