自发性脑出血临床专病数据库应用及研究进展
Applications and Research Progress of Clinical Specialty Databases in Spontaneous Intracerebral Hemorrhage
DOI: 10.12677/acm.2025.1551577, PDF,   
作者: 田泰宇, 李智恒, 朱 甜:吉首大学医学院,湖南 吉首;田 志*:吉首大学临床医院,湖南 吉首
关键词: 自发性脑出血数据库人工智能研究进展综述Spontaneous Intracerebral Hemorrhage Database Artificial Intelligence Research Progress Review
摘要: 随着人口老龄化的发展,自发性脑出血的发病率居高不下,全球年发病率高达24.6/100万。因此,构建自发性脑出血专病数据库具有重要临床价值。通过系统收集的大量医学数据可以提供流行病学资料。与常规病历系统不同,它支持快速生成可验证的临床假设。总之,专病数据库为自发性脑出血的病因、临床过程、结局、预后提供了可靠的医学数据。本文重点介绍专病数据库的特点、在临床中的应用及研究进展及人工智能技术在构建数据库中的应用,以促进多地区自发性脑出血数据库的构建、辅助医疗决策并提高精准医疗水平。
Abstract: The development of population ageing has resulted in a high incidence of spontaneous cerebral haemorrhage, with a global annual incidence rate of 24.6 per million. Consequently, there is a significant clinical need to establish a database for spontaneous cerebral haemorrhage. The database will collate a substantial volume of medical data, providing valuable epidemiological insights. In contrast to conventional medical record systems, it will facilitate the rapid formulation of testable clinical hypotheses. In conclusion, the disease-specific database provides reliable medical data on the etiology, clinical course, outcome, and prognosis of spontaneous cerebral haemorrhage. The focus of this paper is on the characteristics of the speciality databases, their application in clinical practice and research progress, and the application of artificial intelligence technology in constructing the databases. The aim is to facilitate the construction of multi-regional spontaneous cerebral haemorrhage databases, to assist medical decision-making and to improve precision medicine.
文章引用:田泰宇, 李智恒, 朱甜, 田志. 自发性脑出血临床专病数据库应用及研究进展[J]. 临床医学进展, 2025, 15(5): 1936-1941. https://doi.org/10.12677/acm.2025.1551577

参考文献

[1] Chambergo-Michilot, D., Brañez-Condorena, A., Alva-Diaz, C., Sequeiros, J., Abanto, C. and Pacheco-Barrios, K. (2021) Evidence-Based Appraisal of Blood Pressure Reduction in Spontaneous Intracerebral Hemorrhage: A Scoping Review and Overview. Clinical Neurology and Neurosurgery, 202, Article 106497. [Google Scholar] [CrossRef] [PubMed]
[2] Greenberg, S.M., Ziai, W.C., Cordonnier, C., Dowlatshahi, D., Francis, B., Goldstein, J.N., et al. (2022) 2022 Guideline for the Management of Patients with Spontaneous Intracerebral Hemorrhage: A Guideline from the American Heart Association/American Stroke Association. Stroke, 53, e282-e361. [Google Scholar] [CrossRef] [PubMed]
[3] Liu, L., Wang, D., Wong, K.S.L. and Wang, Y. (2011) Stroke and Stroke Care in China: Huge Burden, Significant Workload, and a National Priority. Stroke, 42, 3651-3654. [Google Scholar] [CrossRef] [PubMed]
[4] 董方杰, 胡建平, 吴士勇. 我国卫生健康信息互联互通2.0技术特征研究[J]. 中国卫生信息管理杂志, 2023, 20(1): 1-6.
[5] 刘迷迷, 杜国霞, 周毅, 等. 专病数据库建设与应用研究[J]. 医学信息学杂志, 2021, 42(11): 81-86, 93.
[6] 郭强, 王丛, 衡反修. 医疗大数据平台建设机遇、挑战及其发展[J]. 医学信息学杂志, 2021, 42(1): 2-8.
[7] 薛万国, 乔屾, 车贺宾, 等. 临床科研数据库系统的现状与未来[J]. 中国数字医学, 2021, 16(1): 2-6.
[8] Brainin, M. (1994) Overview of Stroke Data Banks. Neuroepidemiology, 13, 250-258. [Google Scholar] [CrossRef] [PubMed]
[9] 陈亦豪, 常健博, 魏俊吉, 等. 脑卒中大型医学数据库应用及研究进展[J]. 中国现代神经疾病杂志, 2021, 21(3): 141-146.
[10] 谢高强, 李英山, 姚晨. 电子数据采集对我国临床研究的机遇和挑战[J]. 中国新药杂志, 2013, 22(6): 620-623.
[11] Schwamm, L., Reeves, M.J. and Frankel, M. (2006) Designing a Sustainable National Registry for Stroke Quality Improvement. American Journal of Preventive Medicine, 31, S251-S257. [Google Scholar] [CrossRef] [PubMed]
[12] Bronstein, K., Murray, P., Licata-Gehr, E., Banko, M., Kelly-Hayes, M., Fast, S., et al. (1986) The Stroke Data Bank Project: Implications for Nursing Research. Journal of Neuroscience Nursing, 18, 132-134. [Google Scholar] [CrossRef] [PubMed]
[13] Bogousslavsky, J., Van Melle, G. and Regli, F. (1988) The Lausanne Stroke Registry: Analysis of 1,000 Consecutive Patients with First Stroke. Stroke, 19, 1083-1092. [Google Scholar] [CrossRef] [PubMed]
[14] WHO MONICA Project Principal Invest (1988) The World Health Organization Monica Project (Monitoring Trends and Determinants in Cardiovascular Disease): A Major International Collaboration. Journal of Clinical Epidemiology, 41, 105-114. [Google Scholar] [CrossRef] [PubMed]
[15] Kapral, M.K., Laupacis, A., Phillips, S.J., Silver, F.L., Hill, M.D., Fang, J., et al. (2004) Stroke Care Delivery in Institutions Participating in the Registry of the Canadian Stroke Network. Stroke, 35, 1756-1762. [Google Scholar] [CrossRef] [PubMed]
[16] Shiotsuki, H., Ogushi, Y., Fushimi, K., et al. (2005) Evaluation of Applied Cases of Thrombolytic Therapy against Ultra-Acute Ischemic Stroke. Using the Japanese Standard Stroke Registry Database. The Tokai Journal of Experimental and Clinical Medicine, 30, 49-62.
[17] California Acute Stroke Pilot Registry (CASPR) Investigators (2005) Prioritizing Interventions to Improve Rates of Thrombolysis for Ischemic Stroke. Neurology, 64, 654-659. [Google Scholar] [CrossRef] [PubMed]
[18] 高晓兰, 胡长梅, 王文志, 等. 出血性卒中与缺血性卒中危险因素对比分析——多中心脑卒中数据库临床研究[J]. 中国慢性病预防与控制, 1999, 7(4): 14-16.
[19] 刘小玲, 葛朝明. 脑卒中数据库的研究进展[J]. 中国医学创新, 2017, 14(1): 145-148.
[20] Sun, W., Ou, Q., Zhang, Z., Qu, J. and Huang, Y. (2017) Chinese Acute Ischemic Stroke Treatment Outcome Registry (CASTOR): Protocol for a Prospective Registry Study on Patterns of Real-World Treatment of Acute Ischemic Stroke in China. BMC Complementary and Alternative Medicine, 17, Article No. 357. [Google Scholar] [CrossRef] [PubMed]
[21] Hamet, P. and Tremblay, J. (2017) Artificial Intelligence in Medicine: Clinical and Experimental. Metabolism, 69, S36-S40. [Google Scholar] [CrossRef] [PubMed]
[22] 王耀国, 李鹏, 刘迷迷, 等. 临床专病数据库建设现状与思考[J]. 医学信息学杂志, 2024, 45(3): 65-69.
[23] Juhn, Y. and Liu, H. (2020) Artificial Intelligence Approaches Using Natural Language Processing to Advance Ehr-Based Clinical Research. Journal of Allergy and Clinical Immunology, 145, 463-469. [Google Scholar] [CrossRef] [PubMed]
[24] Renard, F., Guedria, S., Palma, N.D. and Vuillerme, N. (2020) Variability and Reproducibility in Deep Learning for Medical Image Segmentation. Scientific Reports, 10, Article No. 13724. [Google Scholar] [CrossRef] [PubMed]
[25] Chang, J.B., Jiang, S.Z., Chen, X.J., Luo, J.X., Li, W.L., Zhang, Q.H., et al. (2020) Consistency Evaluation of an Automatic Segmentation for Quantification of Intracerebral Hemorrhage Using Convolution Neural Network. Chinese Journal of Contemporary Neurology and Neurosurgery, 20, 585-590. [Google Scholar] [CrossRef
[26] 潘锋. 人工智能引领神经外科医疗进入新时代[J]. 中国医药导报, 2023, 20(12): 1-3.
[27] Inaguma, D., Kitagawa, A., Yanagiya, R., Koseki, A., Iwamori, T., Kudo, M., et al. (2020) Increasing Tendency of Urine Protein Is a Risk Factor for Rapid EGFR Decline in Patients with CKD: A Machine Learning-Based Prediction Model by Using a Big Database. PLOS ONE, 15, e0239262. [Google Scholar] [CrossRef] [PubMed]
[28] Geng, Z., Yang, C., Zhao, Z., Yan, Y., Guo, T., Liu, C., et al. (2024) Development and Validation of a Machine Learning-Based Predictive Model for Assessing the 90-Day Prognostic Outcome of Patients with Spontaneous Intracerebral Hemorrhage. Journal of Translational Medicine, 22, Article No. 236. [Google Scholar] [CrossRef] [PubMed]
[29] Matsumoto, K., Ishihara, K., Matsuda, K., Tokunaga, K., Yamashiro, S., Soejima, H., et al. (2024) Machine Learning-Based Prediction for In‐Hospital Mortality after Acute Intracerebral Hemorrhage Using Real‐World Clinical and Image Data. Journal of the American Heart Association, 13, e036447. [Google Scholar] [CrossRef] [PubMed]