Natural Language Processing–Assessed Unmet Medical and Social Needs in Cancer Crowdfunding Stories
This cross-sectional study uses a large natural language processing model to examine unmet medical and social needs based on cancer-related fundraising stories in the US.
Authors
- K. Robin Yabroff (ORCID: https://orcid.org/0000-0003-0644-5572)
- Fumiko Chino (ORCID: https://orcid.org/0000-0003-0498-488X)
- Jingxuan Zhao (ORCID: https://orcid.org/0000-0002-2733-2000)
- Farhad Islami (ORCID: https://orcid.org/0000-0002-7357-5994)
- Zhiyuan Zheng (ORCID: https://orcid.org/0000-0002-7665-3564)
- Shaojun Yu (ORCID: https://orcid.org/0000-0003-3938-5828)
- Matthew P. Banegas (ORCID: https://orcid.org/0000-0002-6158-7162)
- Jing Zhang (ORCID: https://orcid.org/0009-0002-0676-6085)
Institutions
- American Cancer Society (US)
- Memorial Sloan Kettering Cancer Center (US)
- Emory University (US)
- University of California San Diego (US)
Publication Details
- Journal
- JAMA Oncology
- Published
- 2024-10-10
- DOI
- https://doi.org/10.1001/jamaoncol.2024.4412
- Citations
- 5
- Primary Topic
- FinTech, Crowdfunding, Digital Finance
- Type
- article
- Field-Weighted Citation Impact
- 2.64