Using Bayesian Networks to Predict Urgent Care Visits in Patients Receiving Systemic Therapy for Non–Small Cell Lung Cancer
Multidimensional data sources, including demographic, clinical, PRO, and WSD, can enhance ML predictive models to elucidate complex, interactive factors influencing health care utilization during the first 60 days of ST. Use of explainable ML to predict and prevent treatment toxicities and health care utilization could improve patient outcomes and enhance the quality of cancer care delivery.
Authors
- Brian D. Gonzalez (ORCID: https://orcid.org/0000-0001-5108-5735)
- Issam El Naqa (ORCID: https://orcid.org/0000-0001-6023-1132)
- Laura B. Oswald (ORCID: https://orcid.org/0000-0001-6281-2512)
- Yi Luo (ORCID: https://orcid.org/0000-0003-2519-5900)
- Xiaoyin Li (ORCID: https://orcid.org/0000-0001-8028-8910)
- Andreas Saltos (ORCID: https://orcid.org/0000-0003-3622-026X)
- Trung Q. Le (ORCID: https://orcid.org/0000-0002-4169-9941)
- Eric B. Haura (ORCID: https://orcid.org/0000-0003-3114-1909)
- Lisa M. Gudenkauf (ORCID: https://orcid.org/0000-0001-6825-5921)
- Aasha I. Hoogland (ORCID: https://orcid.org/0000-0002-8691-8132)
- Jerrin Joy Pullukkara (ORCID: https://orcid.org/0000-0001-6659-5623)
Institutions
- University of South Florida (US)
- Moffitt Cancer Center (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1200/cci-24-00315
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00