机器学习在肺炎死亡预测模型中的研究进展
Research Progress of Machine Learning in Pneumonia Death Prediction Model
DOI: 10.12677/hjbm.2025.153052, PDF,   
作者: 申艳梅, 马普艳, 许均通：大理大学药学院，云南 大理；郑鹏程*：云南省第一人民医院/昆明理工大学附属医院药学部，云南 昆明
关键词: 肺炎；机器学习；死亡；风险预测；Pneumonia； Machine Learning； Death； Risk Prediction
摘要: 肺炎(pneumonia)是指各种致病因素导致的肺实质及肺间质炎症，是全球范围内常见的呼吸系统疾病之一，而重症肺炎是在肺炎的基础上发展而来，具有病死率高，并发症多和预后差的特点，并加重医疗经济负担，同时对人类健康构成重大威胁。随着人工智能的迅速发展和医疗健康数据的急剧增长，机器学习已经在肺炎死亡预测中得到了广泛应用，帮助临床医生精准的进行个体化临床用药和提高治疗效果，延长患者的生存时间，提高其生存质量。
Abstract: Pneumonia refers to pulmonary parenchyma and interstitial inflammation caused by various pathogenic factors, which is one of the common respiratory diseases in the world, and severe pneumonia develops on the basis of pneumonia, with high mortality, multiple complications and poor prognosis, and increases the economic burden of medical care, and poses a major threat to human health. With the rapid development of artificial intelligence and the rapid growth of medical and health data, machine learning has been widely used in the prediction of pneumonia death, helping clinicians to accurately personalize clinical medication and improve treatment effects, extend the survival time of patients, and improve their quality of life.
文章引用：申艳梅, 马普艳, 许均通, 郑鹏程. 机器学习在肺炎死亡预测模型中的研究进展[J]. 生物医学, 2025, 15(3): 453-458. https://doi.org/10.12677/hjbm.2025.153052

参考文献

[1] GBD 2017 Disease and Injury Incidence and Prevalence Collaborators (2018) Global, Regional, and National Incidence, Prevalence, and Years Lived with Disability for 354 Diseases and Injuries for 195 Countries and Territories, 1990-2017: A Systematic Analysis for the Global Burden of Disease Study 2017. The Lancet, 392, 1789-1858.
[2] Aliberti, S., Dela Cruz, C.S., Amati, F., Sotgiu, G. and Restrepo, M.I. (2021) Community-Acquired Pneumonia. The Lancet, 398, 906-919. [Google Scholar] [CrossRef] [PubMed]
[3] Dinh, A., Duran, C., Ropers, J., et al. (2021) Factors Associated with Treatment Failure in Moderately Severe Community-Acquired Pneumonia: A Secondary Analysis of a Randomized Clinical Trial. JAMA Network Open, 4, e2129566.
[4] 李重锦, 苏新星, 蒋丽君, 等. 重症肺炎患儿预后的危险因素及其列线图预测模型构建研究[J]. 实用心脑肺血管病杂志, 2021, 29(9): 47-52.
[5] 殷菲. 影响重症肺炎患者预后的相关因素分析及构建Nomogram预测模型的价值研究[J]. 临床急诊杂志, 2020, 21(10): 819-825.
[6] 刘佳琦, 曾炳亮, 陈会, 等. 基于CT影像组学建立肺孢子菌肺炎的预后模型[J]. 江西医药, 2023, 58(9): 1022-1025.
[7] Collins, G.S., Moons, K.G.M., Dhiman, P., et al. (2024) TRIPOD+AI Statement: Updated Guidance for Reporting Clinical Prediction Models that Use Regression or Machine Learning Methods. British Medical Journal, 385, e078378.
[8] Wei, J., Cao, H., Peng, M., Zhang, Y., Li, S., Ma, W., et al. (2025) An Interpretable Machine Learning Model for Predicting In-Hospital Mortality in ICU Patients with Ventilator-Associated Pneumonia. PLOS ONE, 20, e0316526. [Google Scholar] [CrossRef] [PubMed]
[9] 罗松平, 刘单霞, 韦兆吉, 等. 重症肺炎行有创机械通气患者ICU死亡的多因素分析及风险模型建立[J]. 中国急救医学, 2023, 43(4): 268-272.
[10] Riley, R.D., Ensor, J., Snell, K.I.E., Harrell, F.E., Martin, G.P., Reitsma, J.B., et al. (2020) Calculating the Sample Size Required for Developing a Clinical Prediction Model. British Medical Journal, 368, m441. [Google Scholar] [CrossRef] [PubMed]
[11] Chen, R., Chen, J., Yang, S., Luo, S., Xiao, Z., Lu, L., et al. (2023) Prediction of Prognosis in COVID-19 Patients Using Machine Learning: A Systematic Review and Meta-Analysis. International Journal of Medical Informatics, 177, Article 105151. [Google Scholar] [CrossRef] [PubMed]
[12] 金静怡, 王丽华, 季建红. ICU呼吸机相关性肺炎风险预测模型的研究进展[J]. 当代护士(上旬刊), 2024, 31(8): 13-17.
[13] 苗若琪, 乔瑞萍. 基于数据挖掘技术的社区获得性肺炎患病风险预测模型的构建[J]. 郑州大学学报(医学版), 2023, 58(3): 306-310.
[14] Wang, B., Li, Y., Tian, Y., Ju, C., Xu, X. and Pei, S. (2023) Novel Pneumonia Score Based on a Machine Learning Model for Predicting Mortality in Pneumonia Patients on Admission to the Intensive Care Unit. Respiratory Medicine, 217, Article 107363. [Google Scholar] [CrossRef] [PubMed]
[15] 蔡佩良, 刘超武, 朱振刚, 等. 基于人工智能构建非危重型新型冠状病毒肺炎的中医辅助决策模型[J]. 中国中医急症, 2024, 33(3): 399-404.
[16] 曾梦, 赵娜, 王显棋, 等. 基于胸部CT图像的肺炎深度学习分类预测模型[J]. 陆军军医大学学报, 2023, 45(21): 2266-2274.
[17] Xiang, B., Liu, Y., Jiao, S., Zhang, W., Wang, S. and Yi, M. (2024) Development and Validation of Interpretable Machine Learning Models for Postoperative Pneumonia Prediction. Frontiers in Public Health, 12, Article 1468504. [Google Scholar] [CrossRef] [PubMed]
[18] 吴明翠, 王若雁, 张莉, 等. 肺超声、X线联合实验室指标预测新型冠状病毒肺炎危重型患者预后的临床价值[J]. 临床超声医学杂志, 2024, 26(5): 370-374.
[19] 王彤, 季达峰. 老年重症肺炎的危险因素及诊断与治疗进展[J]. 现代医学与健康研究电子杂志, 2023, 7(24): 37-40.
[20] 李欣昱, 武轶群, 林连君. 社区获得性肺炎远期死亡风险的研究进展[J]. 国际老年医学杂志, 2023, 44(1): 99-101.
[21] 颜宇飞, 刘明利, 金一鸣, 等. 新型冠状病毒肺炎患者血液NLR, DD和CRP水平联合检测对死亡风险的评估研究[J]. 现代检验医学杂志, 2021, 36(1): 92-95+164.
[22] Misra, N.K., Das, S., Satpathy, S., Addula, S.R. and Trivedi, M.C. (2024) COVID-19 Pandemic: A Worldwide Critical Review with the Machine Learning Model-Based Prediction. Journal of the Institution of Engineers (India): Series B, 106, 339-349. [Google Scholar] [CrossRef] 
[23] Rostami, A., Mousavi, F., Javadinia, S.A., Robatjazi, M. and Mehrpouyan, M. (2024) Predictive Value of Machine Learning Models in Mortality of Coronavirus Disease 2019 (COVID-19) Pneumonia. International Journal of Computational Intelligence Systems, 17, Article No. 221. [Google Scholar] [CrossRef] 
[24] Alhassoon, K., Alhsaon, M.A., Alsunaydih, F., Alsaleem, F., Salim, O., Aly, S., et al. (2024) Machine Learning Predictive Modeling of the Persistence of Post-Covid19 Disorders: Loss of Smell and Taste as Case Studies. Heliyon, 10, e35246. [Google Scholar] [CrossRef] [PubMed]
[25] Malin, B., Karp, D. and Scheuermann, R.H. (2010) Technical and Policy Approaches to Balancing Patient Privacy and Data Sharing in Clinical and Translational Research. Journal of Investigative Medicine, 58, 11-18. [Google Scholar] [CrossRef] [PubMed]
[26] Brauneck, A., Schmalhorst, L., Kazemi Majdabadi, M.M., Bakhtiari, M., Völker, U., Baumbach, J., et al. (2023) Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review. Journal of Medical Internet Research, 25, e41588. [Google Scholar] [CrossRef] [PubMed]
[27] Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H. and Raad, A. (2023) Reviewing Federated Machine Learning and Its Use in Diseases Prediction. Sensors, 23, Article 2112. [Google Scholar] [CrossRef] [PubMed]
[28] Wang, M., Li, W., Wang, H. and Song, P. (2024) Development and Validation of Machine Learning-Based Models for Predicting Healthcare-Associated Bacterial/Fungal Infections among COVID-19 Inpatients: A Retrospective Cohort Study. Antimicrobial Resistance & Infection Control, 13, Article No. 42. [Google Scholar] [CrossRef] [PubMed]
[29] Wang, T., Zhao, Z., Li, W., Wu, J., Ye, Q. and Xie, H. (2023) Machine Learning Predictive Modeling for the Identification of Moderate Coronavirus Disease 2019 during the Pandemic: A Retrospective Study. Cureus, 15, e50619e50619. [Google Scholar] [CrossRef] [PubMed]
[30] Ohno, Y., Aoki, T., Endo, M., Koyama, H., Moriya, H., Okada, F., et al. (2023) Machine Learning-Based Computer-Aided Simple Triage (CAST) for COVID-19 Pneumonia as Compared with Triage by Board-Certified Chest Radiologists. Japanese Journal of Radiology, 42, 276-290. [Google Scholar] [CrossRef] [PubMed]
[31] Ismaeel, N.Q., Mohammed, H.J., Chaloob, I.Z., Kwekha-Rashid, A.S., Alhayani, B., Alkhayyat, A., et al. (2023) Application of Healthcare Management Technologies for COVID-19 Pandemic Using Internet of Things and Machine Learning Algorithms. Wireless Personal Communications, 122. [Google Scholar] [CrossRef] 

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