人工智能在急危重症护理中的应用进展
Progress in the Application of Artificial Intelligence in Critical Care Nursing
摘要: 急危重症医学领域正面临临床数据海量增长与医护人员认知负荷过重的双重挑战。人工智能技术的快速发展为破解这一困境提供了新的解决范式。本文系统综述了人工智能在急危重症护理中的最新应用进展，基于持续生命体征监测的早期预警系统、脓毒症全周期智能管理以及机械通气智能决策支持。研究表明，机器学习模型能够整合电子病历、连续生理波形及实验室数据，在预测临床恶化、脓毒症、心脏骤停等不良事件方面显著优于传统评分系统，且可实现数小时至数十小时的提前预警。可穿戴设备与人工智能的结合进一步拓展了监测边界，实现了从重症监护室到普通病房的全院覆盖。在脓毒症管理方面，人工智能不仅支持早期识别，还延伸至亚型分类、并发症预测及预后评估，为个体化治疗提供依据。机械通气领域的应用则聚焦于需求预测、脱机评估及实时决策辅助，旨在优化通气策略并减轻医护负担。
Abstract: The field of critical care medicine is confronting dual challenges posed by the exponential growth of clinical data and the substantial cognitive burden on healthcare professionals. The rapid advancement of artificial intelligence (AI) technology offers a novel paradigm to address this predicament. This review systematically summarizes the latest progress in AI applications within critical care nursing, early warning systems based on continuous vital signs monitoring, full-cycle intelligent management of sepsis, and intelligent decision support for mechanical ventilation. Current evidence demonstrates that machine learning models, by integrating electronic health records, continuous physiological waveforms, and laboratory data, significantly outperform conventional scoring systems in predicting adverse events such as clinical deterioration, sepsis, and cardiac arrest, with the capability of providing early warnings hours to tens of hours in advance. The integration of wearable devices with AI further extends monitoring boundaries, enabling hospital-wide coverage from intensive care units to general wards. Regarding sepsis management, AI applications have expanded beyond early recognition to encompass phenotyping classification, complication prediction, and prognostic assessment, thereby furnishing evidence for individualized therapeutic strategies. In the domain of mechanical ventilation, AI focuses on demand prediction, weaning evaluation, and real-time decision assistance, aiming to optimize ventilation protocols and alleviate clinical workload.
文章引用：李冰雁. 人工智能在急危重症护理中的应用进展[J]. 临床个性化医学, 2026, 5(2): 114-121. https://doi.org/10.12677/jcpm.2026.52108

参考文献

[1] Biesheuvel, L.A., Dongelmans, D.A. and Elbers, P.W.G. (2024) Artificial Intelligence to Advance Acute and Intensive Care Medicine. Current Opinion in Critical Care, 30, 246-250. [Google Scholar] [CrossRef] [PubMed]
[2] Li, F., Wang, S., Gao, Z., Qing, M., Pan, S., Liu, Y., et al. (2025) Harnessing Artificial Intelligence in Sepsis Care: Advances in Early Detection, Personalized Treatment, and Real-Time Monitoring. Frontiers in Medicine, 11, Article ID: 1510792. [Google Scholar] [CrossRef] [PubMed]
[3] Churpek, M.M., Yuen, T.C., Winslow, C., Meltzer, D.O., Kattan, M.W. and Edelson, D.P. (2016) Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards. Critical Care Medicine, 44, 368-374. [Google Scholar] [CrossRef] [PubMed]
[4] Churpek, M.M., Carey, K.A., Snyder, A., et al. (2024) Multicenter Development and Prospective Validation of eCARTv5: A Gradient Boosted Machine Learning Early Warning Score. medRxiv.
[5] Papareddy, P., Lobo, T.J., Holub, M., Bouma, H., Maca, J., Strodthoff, N., et al. (2025) Transforming Sepsis Management: AI-Driven Innovations in Early Detection and Tailored Therapies. Critical Care, 29, Article No. 366. [Google Scholar] [CrossRef] [PubMed]
[6] Singh, N.P., Mujawar, M.A. and Golani, A. (2025) Role of Artificial Intelligence in Enhancing Mechanical Ventilation—A Peek into the Future. Indian Journal of Anaesthesia, 69, 722-728. [Google Scholar] [CrossRef] [PubMed]
[7] Akel, M.A., Carey, K.A., Winslow, C.J., Churpek, M.M. and Edelson, D.P. (2021) Less Is More: Detecting Clinical Deterioration in the Hospital with Machine Learning Using Only Age, Heart Rate, and Respiratory Rate. Resuscitation, 168, 6-10. [Google Scholar] [CrossRef] [PubMed]
[8] Hyland, S.L., Faltys, M., Hüser, M., Lyu, X., Gumbsch, T., Esteban, C., et al. (2020) Early Prediction of Circulatory Failure in the Intensive Care Unit Using Machine Learning. Nature Medicine, 26, 364-373. [Google Scholar] [CrossRef] [PubMed]
[9] Chiang, D., Jiang, Z., Tian, C. and Wang, C. (2025) Development and Validation of a Dynamic Early Warning System with Time-Varying Machine Learning Models for Predicting Hemodynamic Instability in Critical Care: A Multicohort Study. Critical Care, 29, Article No. 318. [Google Scholar] [CrossRef] [PubMed]
[10] Aagaard, N., Aasvang, E.K. and Meyhoff, C.S. (2024) Discrepancies between Promised and Actual AI Capabilities in the Continuous Vital Sign Monitoring of In-Hospital Patients: A Review of the Current Evidence. Sensors, 24, Article 6497. [Google Scholar] [CrossRef] [PubMed]
[11] Scheid, M.R., Friedmann, B., Oppenheim, M., et al. (2025) Beyond Episodic Early Warning Systems: A Continuous Clinical Alert System for Early Detection of In-Hospital Deterioration. medRxiv.
[12] Rossetti, S.C., Dykes, P.C., Knaplund, C., Cho, S., Withall, J., Lowenthal, G., et al. (2025) Real-Time Surveillance System for Patient Deterioration: A Pragmatic Cluster-Randomized Controlled Trial. Nature Medicine, 31, 1895-1902. [Google Scholar] [CrossRef] [PubMed]
[13] Lee, H., Kuo, P., Qian, F., Li, C., Hu, J., Hsu, W., et al. (2024) Prediction of In-Hospital Cardiac Arrest in the Intensive Care Unit: Machine Learning-Based Multimodal Approach. JMIR Medical Informatics, 12, e49142-e49142. [Google Scholar] [CrossRef] [PubMed]
[14] Chang, H., Park, J.E., Lee, D., Lee, K., Jekal, S.Y., Moon, K.T., et al. (2025) Development and Validation of a Transformer Model-Based Early Warning Score for Real-Time Prediction of Adverse Outcomes in the Emergency Department. Scientific Reports, 15, Article No. 23021. [Google Scholar] [CrossRef] [PubMed]
[15] Henry, K.E. and Giannini, H.M. (2024) Early Warning Systems for Critical Illness Outside the Intensive Care Unit. Critical Care Clinics, 40, 561-581. [Google Scholar] [CrossRef] [PubMed]
[16] Zhang, H., Wang, C. and Yang, N. (2024) Diagnostic Performance of Machine-Learning Algorithms for Sepsis Prediction: An Updated Meta-Analysis. Technology and Health Care, 32, 4291-4307. [Google Scholar] [CrossRef] [PubMed]
[17] Nemati, S., Holder, A., Razmi, F., Stanley, M.D., Clifford, G.D. and Buchman, T.G. (2018) An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU. Critical Care Medicine, 46, 547-553. [Google Scholar] [CrossRef] [PubMed]
[18] Persson, I., Macura, A., Becedas, D. and Sjövall, F. (2024) Early Prediction of Sepsis in Intensive Care Patients Using the Machine Learning Algorithm NAVOY® Sepsis, a Prospective Randomized Clinical Validation Study. Journal of Critical Care, 80, Article 154400. [Google Scholar] [CrossRef] [PubMed]
[19] Moor, M., Bennett, N., Plečko, D., Horn, M., Rieck, B., Meinshausen, N., et al. (2023) Predicting Sepsis Using Deep Learning across International Sites: A Retrospective Development and Validation Study. eClinicalMedicine, 62, Article 102124. [Google Scholar] [CrossRef] [PubMed]
[20] van Amstel, R.B.E., Rademaker, E., Kennedy, J.N., Bos, L.D.J., Peters-Sengers, H., Butler, J.M., et al. (2025) Clinical Subtypes in Critically Ill Patients with Sepsis: Validation and Parsimonious Classifier Model Development. Critical Care, 29, Article No. 58. [Google Scholar] [CrossRef] [PubMed]
[21] Lei, J., Zhai, J., Zhang, Y., Qi, J. and Sun, C. (2025) Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients with Sepsis: Development and Validation Study Based on a Multicenter Cohort Study. Journal of Medical Internet Research, 27, e66733. [Google Scholar] [CrossRef] [PubMed]
[22] Zhou, S., Lu, Z., Liu, Y., Wang, M., Zhou, W., Cui, X., et al. (2024) Interpretable Machine Learning Model for Early Prediction of 28-Day Mortality in ICU Patients with Sepsis-Induced Coagulopathy: Development and Validation. European Journal of Medical Research, 29, Article No. 14. [Google Scholar] [CrossRef] [PubMed]
[23] Ge, X., Chen, W., Shi, J., Zhang, J., Tai, H., Zhang, Y., et al. (2025) Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study. Journal of Medical Internet Research, 27, e73840. [Google Scholar] [CrossRef] 
[24] Wang, Y., Gao, Z., Zhang, Y., Lu, Z. and Sun, F. (2024) Early Sepsis Mortality Prediction Model Based on Interpretable Machine Learning Approach: Development and Validation Study. Internal and Emergency Medicine, 20, 909-918. [Google Scholar] [CrossRef] [PubMed]
[25] Bao, C., Deng, F. and Zhao, S. (2023) Machine-Learning Models for Prediction of Sepsis Patients Mortality. Medicina Intensiva, 47, 315-325. [Google Scholar] [CrossRef] 
[26] Zhang, G., Shao, F., Yuan, W., Wu, J., Qi, X., Gao, J., et al. (2024) Predicting Sepsis In-Hospital Mortality with Machine Learning: A Multi-Center Study Using Clinical and Inflammatory Biomarkers. European Journal of Medical Research, 29, Article No. 156. [Google Scholar] [CrossRef] [PubMed]
[27] Fang, S., Jin, H., Zhang, J., Wang, Y., Nan, W., Feng, Y., et al. (2024) Machine Learning for Predicting Acute Myocardial Infarction in Patients with Sepsis. Scientific Reports, 14, Article No. 30629. [Google Scholar] [CrossRef] [PubMed]
[28] Yijing, L., Wenyu, Y., Kang, Y., Shengyu, Z., Xianliang, H., Xingliang, J., et al. (2022) Prediction of Cardiac Arrest in Critically Ill Patients Based on Bedside Vital Signs Monitoring. Computer Methods and Programs in Biomedicine, 214, Article 106568. [Google Scholar] [CrossRef] [PubMed]
[29] Kim, Y.K., Seo, W., Lee, S.J., Koo, J.H., Kim, G.C., Song, H.S., et al. (2024) Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study. Journal of Medical Internet Research, 26, e62890. [Google Scholar] [CrossRef] [PubMed]
[30] Cho, K., Kim, J.S., Lee, D.H., Lee, S., Song, M.J., Lim, S.Y., et al. (2023) Prospective, Multicenter Validation of the Deep Learning-Based Cardiac Arrest Risk Management System for Predicting In-Hospital Cardiac Arrest or Unplanned Intensive Care Unit Transfer in Patients Admitted to General Wards. Critical Care, 27, Article No. 346. [Google Scholar] [CrossRef] [PubMed]
[31] Sun, Y., He, Z., Ren, J. and Wu, Y. (2023) Prediction Model of In-Hospital Mortality in Intensive Care Unit Patients with Cardiac Arrest: A Retrospective Analysis of MIMIC-IV Database Based on Machine Learning. BMC Anesthesiology, 23, Article No. 178. [Google Scholar] [CrossRef] [PubMed]
[32] Ni, P., Zhang, S., Zhang, G., Zhang, W., Zhang, H., Zhu, Y., et al. (2025) Development and Validation of Machine Learning-Based Prediction Model for Outcome of Cardiac Arrest in Intensive Care Units. Scientific Reports, 15, Article No. 8691. [Google Scholar] [CrossRef] [PubMed]
[33] Kim, Y., Kim, H., Choi, J., Cho, K., Yoo, D., Lee, Y., et al. (2023) Early Prediction of Need for Invasive Mechanical Ventilation in the Neonatal Intensive Care Unit Using Artificial Intelligence and Electronic Health Records: A Clinical Study. BMC Pediatrics, 23, Article No. 525. [Google Scholar] [CrossRef] [PubMed]
[34] Hur, S., Min, J.Y., Yoo, J., Kim, K., Chung, C.R., Dykes, P.C., et al. (2021) Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study. Journal of Medical Internet Research, 23, e23508. [Google Scholar] [CrossRef] [PubMed]
[35] Liu, W., Tao, G., Zhang, Y., Xiao, W., Zhang, J., Liu, Y., et al. (2022) A Simple Weaning Model Based on Interpretable Machine Learning Algorithm for Patients with Sepsis: A Research of MIMIC-IV and eICU Databases. Frontiers in Medicine, 8, Article ID: 814566. [Google Scholar] [CrossRef] [PubMed]
[36] Menguy, J., De Longeaux, K., Bodenes, L., Hourmant, B. and L’Her, E. (2023) Defining Predictors for Successful Mechanical Ventilation Weaning, Using a Data-Mining Process and Artificial Intelligence. Scientific Reports, 13, Article No. 20483. [Google Scholar] [CrossRef] [PubMed]
[37] Hotz, J.C., Chang, D., Khemani, R.G. and Newth, C.J.L. (2026) Decision Assist during Mechanical Ventilation. Respiratory Care, 71, 226-236. [Google Scholar] [CrossRef] 
[38] Lim, L., Gim, U., Cho, K., Yoo, D., Ryu, H.G. and Lee, H. (2024) Real-Time Machine Learning Model to Predict Short-Term Mortality in Critically Ill Patients: Development and International Validation. Critical Care, 28, Article No. 76. [Google Scholar] [CrossRef] [PubMed]
[39] Guan, C., Gong, A., Zhao, Y., Yin, C., Geng, L., Liu, L., et al. (2024) Interpretable Machine Learning Model for New-Onset Atrial Fibrillation Prediction in Critically Ill Patients: A Multi-Center Study. Critical Care, 28, Article No. 349. [Google Scholar] [CrossRef] [PubMed]
[40] Țocu, G., Lisă, E.L., Tutunaru, D., Mihailov, R., Șerban, C., Luțenco, V., et al. (2025) The Potential of Artificial Intelligence in the Diagnosis and Prognosis of Sepsis: A Narrative Review. Diagnostics, 15, Article 2169. [Google Scholar] [CrossRef] 
[41] Moor, M., Rieck, B., Horn, M., Jutzeler, C.R. and Borgwardt, K. (2021) Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review. Frontiers in Medicine, 8, Article ID: 607952. [Google Scholar] [CrossRef] [PubMed]
[42] Zubair, M., Din, I., Sarwar, N., Elov, B., Makhmudov, S. and Trabelsi, Z. (2025) Revolutionizing Sepsis Diagnosis Using Machine Learning and Deep Learning Models: A Systematic Literature Review. BMC Infectious Diseases, 25, Article No. 1396. [Google Scholar] [CrossRef] 
[43] Tungushpayev, M., Suleimenova, D., Sarria-Santamerra, A., Aimyshev, T., Gaipov, A. and Viderman, D. (2025) The Value of Machine and Deep Learning in Management of Critically Ill Patients: An Umbrella Review. International Journal of Medical Informatics, 204, Article 106081. [Google Scholar] [CrossRef] [PubMed]