Clinical Retrieval under Overlapping Inflammation Vocabulary
摘要
Clinical Retrieval under Overlapping Inflammation Vocabulary addresses a narrow but consequential interpretive problem: lexical similarity producing biologically irrelevant citations. This literature-based conceptual analysis does not report a new experiment. The starting source uses a review of biofilm interactions with innate and adaptive responses, including pattern recognition, extracellular traps, macrophage polarization, and T-cell differentiation. Specialized vocabularies share words across biologically different processes. Lexical and semantic rankers can retrieve passages that look relevant but operate at the wrong tissue, disease, or evidence level. Add deliberately confusable sources to evaluation. Examine whether generation uses the right population and mechanism rather than only reproducing familiar terminology. The proposed comparison concerns biofilms, immune exhaustion, disease-specific terms; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. The original source's limits remain material: Mechanisms vary across COPD, bronchiectasis, asthma, and other contexts; proposed combined antibiofilm/immunomodulatory strategies are not proven universal treatments.
参考
Yu, Xiao, et al. “Biofilm-Mediated Immune Dysregulation in Chronic Pulmonary Diseases: Mechanisms and Clinical Implications.” Frontiers in Microbiology, vol. 16, 2026, https://doi.org/10.3389/fmicb.2025.1736384.
Sang, Yinghao. “AutoCrit: A Meta-Reasoning Framework for Self-Critique and Iterative Error Correction in LLM Chains-of-Thought.” 2025 6th International Conference on Machine Learning and Computer Application (ICMLCA), IEEE, 2025, pp. 1177–80, https://doi.org/10.1109/icmlca66850.2025.11336788.
Rothman, Kenneth J., Sander Greenland, and Timothy L. Lash. Modern Epidemiology. 3rd ed., Lippincott Williams & Wilkins, 2008.
Benjamini, Yoav, and Yosef Hochberg. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society: Series B, vol. 57, no. 1, 1995, pp. 289–300.
Rajkomar, Alvin, Jeffrey Dean, and Isaac Kohane. “Machine Learning in Medicine.” New England Journal of Medicine, vol. 380, 2019, pp. 1347–58.
Wiens, Jenna, et al. “Do No Harm: A Roadmap for Responsible Machine Learning for Health Care.” Nature Medicine, vol. 25, 2019, pp. 1337–40.
Guo, Chuan, et al. “On Calibration of Modern Neural Networks.” Proceedings of the 34th International Conference on Machine Learning, 2017, pp. 1321–30.
Ovadia, Yaniv, et al. “Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty under Dataset Shift.” Advances in Neural Information Processing Systems, vol. 32, 2019.
Geifman, Yonatan, and Ran El-Yaniv. “Selective Classification for Deep Neural Networks.” Advances in Neural Information Processing Systems, vol. 30, 2017.
Lundberg, Scott M., and Su-In Lee. “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems, vol. 30, 2017.
World Health Organization. Ethics and Governance of Artificial Intelligence for Health. World Health Organization, 2021.
National Academies of Sciences, Engineering, and Medicine. Reproducibility and Replicability in Science. National Academies Press, 2019.