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Artificial intelligence-driven approach for patient-focused drug development
Patients' increasing digital participation provides an opportunity to pursue patient-centric research and drug development by understanding their needs. Social media has proven to be one of the most useful data sources when it comes to understanding a company's potential audience to drive...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601646/ https://www.ncbi.nlm.nih.gov/pubmed/37899963 http://dx.doi.org/10.3389/frai.2023.1237124 |
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author | Karmalkar, Prathamesh Gurulingappa, Harsha Spies, Erica Flynn, Jennifer A. |
author_facet | Karmalkar, Prathamesh Gurulingappa, Harsha Spies, Erica Flynn, Jennifer A. |
author_sort | Karmalkar, Prathamesh |
collection | PubMed |
description | Patients' increasing digital participation provides an opportunity to pursue patient-centric research and drug development by understanding their needs. Social media has proven to be one of the most useful data sources when it comes to understanding a company's potential audience to drive more targeted impact. Navigating through an ocean of information is a tedious task where techniques such as artificial intelligence and text analytics have proven effective in identifying relevant posts for healthcare business questions. Here, we present an enterprise-ready, scalable solution demonstrating the feasibility and utility of social media-based patient experience data for use in research and development through capturing and assessing patient experiences and expectations on disease, treatment options, and unmet needs while creating a playbook for roll-out to other indications and therapeutic areas. |
format | Online Article Text |
id | pubmed-10601646 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106016462023-10-27 Artificial intelligence-driven approach for patient-focused drug development Karmalkar, Prathamesh Gurulingappa, Harsha Spies, Erica Flynn, Jennifer A. Front Artif Intell Artificial Intelligence Patients' increasing digital participation provides an opportunity to pursue patient-centric research and drug development by understanding their needs. Social media has proven to be one of the most useful data sources when it comes to understanding a company's potential audience to drive more targeted impact. Navigating through an ocean of information is a tedious task where techniques such as artificial intelligence and text analytics have proven effective in identifying relevant posts for healthcare business questions. Here, we present an enterprise-ready, scalable solution demonstrating the feasibility and utility of social media-based patient experience data for use in research and development through capturing and assessing patient experiences and expectations on disease, treatment options, and unmet needs while creating a playbook for roll-out to other indications and therapeutic areas. Frontiers Media S.A. 2023-10-12 /pmc/articles/PMC10601646/ /pubmed/37899963 http://dx.doi.org/10.3389/frai.2023.1237124 Text en Copyright © 2023 Karmalkar, Gurulingappa, Spies and Flynn. https://creativecommons.org/licenses/by/4.0/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) and the copyright owner(s) 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. |
spellingShingle | Artificial Intelligence Karmalkar, Prathamesh Gurulingappa, Harsha Spies, Erica Flynn, Jennifer A. Artificial intelligence-driven approach for patient-focused drug development |
title | Artificial intelligence-driven approach for patient-focused drug development |
title_full | Artificial intelligence-driven approach for patient-focused drug development |
title_fullStr | Artificial intelligence-driven approach for patient-focused drug development |
title_full_unstemmed | Artificial intelligence-driven approach for patient-focused drug development |
title_short | Artificial intelligence-driven approach for patient-focused drug development |
title_sort | artificial intelligence-driven approach for patient-focused drug development |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601646/ https://www.ncbi.nlm.nih.gov/pubmed/37899963 http://dx.doi.org/10.3389/frai.2023.1237124 |
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