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Identifying Adverse Drug Events by Relational Learning.通过关系学习识别药物不良事件
Proc AAAI Conf Artif Intell. 2012 Jul;2012:790-793.
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Real-world safety of ulinastatin: a post-marketing surveillance of 11,252 patients in China.乌司他丁在中国 11252 例患者的上市后监测中的真实世界安全性。
BMC Pharmacol Toxicol. 2022 Jul 16;23(1):51. doi: 10.1186/s40360-022-00585-3.
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A data-driven method to detect adverse drug events from prescription data.一种从处方数据中检测药物不良事件的数据驱动方法。
J Biomed Inform. 2018 Sep;85:10-20. doi: 10.1016/j.jbi.2018.07.013. Epub 2018 Jul 29.
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A method for systematic discovery of adverse drug events from clinical notes.一种从临床记录中系统发现药物不良事件的方法。
J Am Med Inform Assoc. 2015 Nov;22(6):1196-204. doi: 10.1093/jamia/ocv102. Epub 2015 Jul 31.
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Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.利用自然语言处理和机器学习方法在电子健康/医疗记录中进行药物不良事件检测：一项范围综述
Drug Saf. 2025 Apr;48(4):321-337. doi: 10.1007/s40264-024-01505-6. Epub 2025 Jan 9.
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Serious Adverse Drug Events Reported to the FDA: Analysis of the FDA Adverse Event Reporting System 2006-2014 Database.向 FDA 报告的严重药物不良事件：FDA 不良事件报告系统 2006-2014 年数据库分析。
J Manag Care Spec Pharm. 2018 Jul;24(7):682-690. doi: 10.18553/jmcp.2018.24.7.682.
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Automatic Detection of Adverse Drug Events in Geriatric Care: Study Proposal.老年护理中药物不良事件的自动检测：研究方案
JMIR Res Protoc. 2022 Nov 15;11(11):e40456. doi: 10.2196/40456.
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Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning.从电子健康记录笔记中提取与药物不良事件相关的信息：基于深度学习的端到端模型设计
JMIR Med Inform. 2018 Nov 26;6(4):e12159. doi: 10.2196/12159.
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Post-Marketing Safety Surveillance of the Salvia Miltiorrhiza Depside Salt for Infusion: A Real World Study.注射用丹参多酚酸盐上市后安全性监测：一项真实世界研究
PLoS One. 2017 Jan 26;12(1):e0170182. doi: 10.1371/journal.pone.0170182. eCollection 2017.
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Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.在流行地区，服用抗叶酸抗疟药物的人群中，叶酸补充剂与疟疾易感性和严重程度的关系。
Cochrane Database Syst Rev. 2022 Feb 1;2(2022):CD014217. doi: 10.1002/14651858.CD014217.
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TICTAC: target illumination clinical trial analytics with cheminformatics.TICTAC：利用化学信息学进行目标照明临床试验分析
Front Bioinform. 2025 Jun 9;5:1579865. doi: 10.3389/fbinf.2025.1579865. eCollection 2025.
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Machine Learning in Causal Inference: Application in Pharmacovigilance.机器学习在因果推断中的应用：在药物警戒中的应用。
Drug Saf. 2022 May;45(5):459-476. doi: 10.1007/s40264-022-01155-6. Epub 2022 May 17.
3
The Beginning of a New Era: Artificial Intelligence in Healthcare.新时代的开端：医疗保健领域的人工智能
Adv Pharm Bull. 2021 May;11(3):414-425. doi: 10.34172/apb.2021.049. Epub 2020 Jul 15.
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Hypothesis-free signal detection in healthcare databases: finding its value for pharmacovigilance.医疗保健数据库中的无假设信号检测：探寻其在药物警戒中的价值。
Ther Adv Drug Saf. 2019 Aug 5;10:2042098619864744. doi: 10.1177/2042098619864744. eCollection 2019.
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Exploiting machine learning for end-to-end drug discovery and development.利用机器学习进行端到端的药物发现和开发。
Nat Mater. 2019 May;18(5):435-441. doi: 10.1038/s41563-019-0338-z. Epub 2019 Apr 18.
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Drug-Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities.药物相互作用发现：基于异构相似性的核学习
Smart Health (Amst). 2018 Dec;9-10:88-100. doi: 10.1016/j.smhl.2018.07.007. Epub 2018 Jul 7.
7
A Decision-Support Tool for Renal Mass Classification.用于肾脏肿块分类的决策支持工具。
J Digit Imaging. 2018 Dec;31(6):929-939. doi: 10.1007/s10278-018-0100-0.
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Markov Logic Networks for Adverse Drug Event Extraction from Text.用于从文本中提取药物不良事件的马尔可夫逻辑网络
Knowl Inf Syst. 2017 May;51(2):435-457. doi: 10.1007/s10115-016-0980-6. Epub 2016 Aug 8.
9
Progress in oral personalized medicine: contribution of 'omics'.口腔个性化医学的进展：“组学”的贡献
J Oral Microbiol. 2015 Sep 4;7:28223. doi: 10.3402/jom.v7.28223. eCollection 2015.
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Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling.提升时计分（SAYL）：一种用于提升建模的统计关系学习方法。
Mach Learn Knowl Discov Databases. 2013;8190:595-611. doi: 10.1007/978-3-642-40994-3_38.
本文引用的文献
1
Disproportionality methods for pharmacovigilance in longitudinal observational databases.纵向观察性数据库中药物警戒的比例失衡方法。
Stat Methods Med Res. 2013 Feb;22(1):39-56. doi: 10.1177/0962280211403602. Epub 2011 Aug 30.
2
What can we really learn from observational studies?: the need for empirical assessment of methodology for active drug safety surveillance and comparative effectiveness research.我们究竟能从观察性研究中学到什么？：对主动药物安全性监测和比较效果研究方法进行实证评估的必要性。
Epidemiology. 2011 Sep;22(5):629-31. doi: 10.1097/EDE.0b013e318228ca1d.
3
Advanced statistics: the propensity score--a method for estimating treatment effect in observational research.高级统计学：倾向得分——一种在观察性研究中估计治疗效果的方法。
Acad Emerg Med. 2004 Sep;11(9):953-61. doi: 10.1197/j.aem.2004.02.530.
4
Application of data mining techniques in pharmacovigilance.数据挖掘技术在药物警戒中的应用。
Br J Clin Pharmacol. 2004 Feb;57(2):127-34. doi: 10.1046/j.1365-2125.2003.01968.x.
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Incidence and preventability of adverse drug events among older persons in the ambulatory setting.门诊环境中老年人药物不良事件的发生率及可预防性
JAMA. 2003 Mar 5;289(9):1107-16. doi: 10.1001/jama.289.9.1107.
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Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies.住院患者药物不良反应的发生率：前瞻性研究的荟萃分析
JAMA. 1998 Apr 15;279(15):1200-5. doi: 10.1001/jama.279.15.1200.
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