Causal predictive modeling of survival of lung and bronchus cancer patients diagnosed during 2010–2011 in Texas
Understanding lung and bronchus cancer survival using advanced causal inference and predictive modeling techniques, highlights the critical importance of early-stage diagnosis, showing that patients diagnosed at localized stages have a substantially higher survival probability. This research underscores the necessity of promoting early screening and equitable cancer care to improve survival rates and healthcare outcomes for lung and bronchus cancer patients.
Authors
- Manish K. Tripathi (ORCID: https://orcid.org/0000-0002-2016-6476)
- Everardo Cobos (ORCID: https://orcid.org/0000-0001-5799-0074)
- Tamer F. Oraby (ORCID: https://orcid.org/0000-0002-8176-1324)
- Zeinab A. Mohamed (ORCID: https://orcid.org/0009-0005-5633-9008)
- Sidketa Ida Fofana
Institutions
- The University of Texas Rio Grande Valley (US)
- Oberlin College (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-10-31
- DOI
- https://doi.org/10.1371/journal.pone.0333477
- Primary Topic
- Advanced Causal Inference Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00