Translational assessment of nursing care models for oncology patients with cardiovascular risk factors
- 1Surgical Oncology, Xinxiang Central Hospital
- 2Pharmaceutical Department, Xinxiang Central Hospital
Abstract
Introduction: The objective of this study was to estimate patient-specific effects of alternative acute nursing care strategies for critically ill oncology patients with cardiovascular risk factors using routinely collected electronic health record data. Existing nursing care evaluations primarily characterize population-level associations or predict observed outcomes, but they do not estimate the potential outcomes of individual patients under standard and enhanced acute care while accounting for evolving clinical states and predictive uncertainty.
Methods: We developed the Counterfactual Event Planner to estimate individualized treatment effects and compare alternative nursing care strategies through clinically constrained trajectory modeling and probabilistic outcome estimation. The proposed framework integrates the Manifold Based Decision Model, Agent based Contextual Navigator, and Probabilistic Outcome Synthesizer to construct clinically meaningful latent representations, explore context-aware intervention trajectories, and estimate potential outcome distributions. Furthermore, Counterfactual Pacing Strategy and Bayesian Outcome Updating enable dynamic trajectory simulation and uncertainty-aware outcome refinement, improving the reliability of individualized treatment effect estimation.
Results and discussion: Extensive experiments on the MIMIC-IV and eICU Collaborative Research Database demonstrate that the proposed framework consistently outperforms representative statistical, neural, and causal inference baselines in individualized treatment effect estimation, policy evaluation, and outcome prediction while maintaining robust cross-hospital generalization. The proposed framework provides an effective and generalizable solution for counterfactual evaluation of nursing care strategies, supporting personalized clinical decision making for oncology patients with cardiovascular risk factors.
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BibTeX
@article{du2026translational,
title = {{Translational assessment of nursing care models for oncology patients with cardiovascular risk factors}},
author = {Huili Du and Jing Wang and Yuguang Xing},
journal = {Frontiers in Cardiovascular Medicine},
year = {2026},
month = oct,
doi = {10.3389/fcvm.2026.1834086},
url = {https://www.frontiersin.org/articles/10.3389/fcvm.2026.1834086/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Translational assessment of nursing care models for oncology patients with cardiovascular risk factors
AU - Huili Du
AU - Jing Wang
AU - Yuguang Xing
PY - 2026
DA - 2026/10/19
JO - Frontiers in Cardiovascular Medicine
DO - 10.3389/fcvm.2026.1834086
UR - https://www.frontiersin.org/articles/10.3389/fcvm.2026.1834086/full
AB - Introduction: The objective of this study was to estimate patient-specific effects of alternative acute nursing care strategies for critically ill oncology patients with cardiovascular risk factors using routinely collected electronic health record data. Existing nursing care evaluations primarily characterize population-level associations or predict observed outcomes, but they do not estimate the potential outcomes of individual patients under standard and enhanced acute care while accounting for evolving clinical states and predictive uncertainty. Methods: We developed the Counterfactual Event Planner to estimate individualized treatment effects and compare alternative nursing care strategies through clinically constrained trajectory modeling and probabilistic outcome estimation. The proposed framework integrates the Manifold Based Decision Model, Agent based Contextual Navigator, and Probabilistic Outcome Synthesizer to construct clinically meaningful latent representations, explore context-aware intervention trajectories, and estimate potential outcome distributions. Furthermore, Counterfactual Pacing Strategy and Bayesian Outcome Updating enable dynamic trajectory simulation and uncertainty-aware outcome refinement, improving the reliability of individualized treatment effect estimation. Results and discussion: Extensive experiments on the MIMIC-IV and eICU Collaborative Research Database demonstrate that the proposed framework consistently outperforms representative statistical, neural, and causal inference baselines in individualized treatment effect estimation, policy evaluation, and outcome prediction while maintaining robust cross-hospital generalization. The proposed framework provides an effective and generalizable solution for counterfactual evaluation of nursing care strategies, supporting personalized clinical decision making for oncology patients with cardiovascular risk factors.
SN - 2297-055X
ER -