Toward Community-Based Natural Language Processing (CBNLP): Cocreating With Communities

Rapid development and adoption of natural language processing (NLP) techniques has led to a multitude of exciting and innovative societal and health care applications. These advancements have also generated concerns around perpetuation of historical injustices and that these tools lack cultural cons...

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Veröffentlicht in:Journal of medical Internet research Jg. 25; H. 1; S. e48498
Hauptverfasser: Pillai, Malvika, Griffin, Ashley C, Kronk, Clair A, McCall, Terika
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Canada Gunther Eysenbach MD MPH, Associate Professor 04.08.2023
JMIR Publications
Schlagworte:
Cancer
Collaboration
Community
Community based action research
Community research
Community-Based Participatory Research
Consultants
Decision making
Empowerment
Ethics
Feedback
Health education
Health information
Health services
Humans
Language
Medical personnel
Natural Language Processing
Participatory research
Patients
Perpetuation
Public health
R&D
Research & development
Researchers
Social networks
Verbal communication
Viewpoint
Web portals
community based
co-design
natural language processing
research design
collaborative
lived experience
lived experiences
machine learning
artificial intelligence
participatory
co-creation
ChatGPT
collaboration
community-based participatory research
ISSN:1438-8871, 1439-4456, 1438-8871
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Abstract Rapid development and adoption of natural language processing (NLP) techniques has led to a multitude of exciting and innovative societal and health care applications. These advancements have also generated concerns around perpetuation of historical injustices and that these tools lack cultural considerations. While traditional health care NLP techniques typically include clinical subject matter experts to extract health information or aid in interpretation, few NLP tools involve community stakeholders with lived experiences. In this perspective paper, we draw upon the field of community-based participatory research, which gathers input from community members for development of public health interventions, to identify and examine ways to equitably involve communities in developing health care NLP tools. To realize the potential of community-based NLP (CBNLP), research and development teams must thoughtfully consider mechanisms and resources needed to effectively collaborate with community members for maximal societal and ethical impact of NLP-based tools.
AbstractList Rapid development and adoption of natural language processing (NLP) techniques has led to a multitude of exciting and innovative societal and health care applications. These advancements have also generated concerns around perpetuation of historical injustices and that these tools lack cultural considerations. While traditional health care NLP techniques typically include clinical subject matter experts to extract health information or aid in interpretation, few NLP tools involve community stakeholders with lived experiences. In this perspective paper, we draw upon the field of community-based participatory research, which gathers input from community members for development of public health interventions, to identify and examine ways to equitably involve communities in developing health care NLP tools. To realize the potential of community-based NLP (CBNLP), research and development teams must thoughtfully consider mechanisms and resources needed to effectively collaborate with community members for maximal societal and ethical impact of NLP-based tools.
Rapid development and adoption of natural language processing (NLP) techniques has led to a multitude of exciting and innovative societal and health care applications. These advancements have also generated concerns around perpetuation of historical injustices and that these tools lack cultural considerations. While traditional health care NLP techniques typically include clinical subject matter experts to extract health information or aid in interpretation, few NLP tools involve community stakeholders with lived experiences. In this perspective paper, we draw upon the field of community-based participatory research, which gathers input from community members for development of public health interventions, to identify and examine ways to equitably involve communities in developing health care NLP tools. To realize the potential of community-based NLP (CBNLP), research and development teams must thoughtfully consider mechanisms and resources needed to effectively collaborate with community members for maximal societal and ethical impact of NLP-based tools.Rapid development and adoption of natural language processing (NLP) techniques has led to a multitude of exciting and innovative societal and health care applications. These advancements have also generated concerns around perpetuation of historical injustices and that these tools lack cultural considerations. While traditional health care NLP techniques typically include clinical subject matter experts to extract health information or aid in interpretation, few NLP tools involve community stakeholders with lived experiences. In this perspective paper, we draw upon the field of community-based participatory research, which gathers input from community members for development of public health interventions, to identify and examine ways to equitably involve communities in developing health care NLP tools. To realize the potential of community-based NLP (CBNLP), research and development teams must thoughtfully consider mechanisms and resources needed to effectively collaborate with community members for maximal societal and ethical impact of NLP-based tools.
Author Griffin, Ashley C
Pillai, Malvika
McCall, Terika
Kronk, Clair A
AuthorAffiliation 4 Center for Medical Informatics Yale School of Medicine New Haven, CT United States
3 Department of Health Policy Stanford University School of Medicine Stanford, CA United States
5 Division of Health Informatics Department of Biostatistics Yale School of Public Health New Haven, CT United States
6 Section of Biomedical Informatics and Data Science Yale School of Medicine New Haven, CT United States
2 Veterans Affairs Palo Alto Health Care System Palo Alto, CA United States
1 Center for Biomedical Informatics Research Stanford University School of Medicine Stanford, CA United States
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– name: 5 Division of Health Informatics Department of Biostatistics Yale School of Public Health New Haven, CT United States
– name: 2 Veterans Affairs Palo Alto Health Care System Palo Alto, CA United States
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CitedBy_id crossref_primary_10_2196_69497
crossref_primary_10_1016_j_jadohealth_2025_06_018
crossref_primary_10_2196_50141
crossref_primary_10_1016_j_jbi_2024_104653
crossref_primary_10_1093_jamia_ocae265
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ContentType Journal Article
Copyright Malvika Pillai, Ashley C Griffin, Clair A Kronk, Terika McCall. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.08.2023.
2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Malvika Pillai, Ashley C Griffin, Clair A Kronk, Terika McCall. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.08.2023. 2023
Copyright_xml – notice: Malvika Pillai, Ashley C Griffin, Clair A Kronk, Terika McCall. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.08.2023.
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– notice: Malvika Pillai, Ashley C Griffin, Clair A Kronk, Terika McCall. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.08.2023. 2023
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Issue 1
Keywords community based
co-design
natural language processing
research design
collaborative
lived experience
lived experiences
machine learning
artificial intelligence
participatory
co-creation
ChatGPT
collaboration
community-based participatory research
Language English
License Malvika Pillai, Ashley C Griffin, Clair A Kronk, Terika McCall. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.08.2023.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
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SubjectTerms Cancer
Collaboration
Community
Community based action research
Community research
Community-Based Participatory Research
Consultants
Decision making
Empowerment
Ethics
Feedback
Health education
Health information
Health services
Humans
Language
Medical personnel
Natural Language Processing
Participatory research
Patients
Perpetuation
Public health
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