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PRM95 - DETECTING INCIDENTS OF INJECTION FROM ELECTRONIC MEDICAL RECORDS USING MACHINE LEARNING METHODS

机译：PRM95 - 使用机器学习方法检测电子医疗记录的注射事件

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K Okamoto; K Goka; M Hirose; T Yamamoto; S Hiragi; G Yamamoto; O Sugiyama; M Nambu; T Kuroda;
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年度 2018
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1. Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records [J] . Zubair Afzal, Martijn J Schuemie, Jan C van Blijderveen, BMC Medical Informatics and Decision Making . 2013,第1期

机译：提高机器学习方法从自由文本电子病历中自动识别病例的敏感性
2. Using statistical and machine learning to help institutions detect suspicious access to electronic health records. [J] . Boxwala AA, Kim J, Grillo JM, Journal of the American Medical Informatics Association : . 2011,第4期

机译：使用统计和机器学习来帮助机构检测对电子病历的可疑访问。
3. Survey of machine learning methods for detecting false data injection attacks in power systems [J] . Ali Sayghe, Yaodan Hu, Ioannis Zografopoulos, IET Smart Grid . 2020,第5期

机译：检测电力系统假数据注入攻击的机器学习方法调查
4. Electronic Medical Record based Machine Learning Methods for Adverse Pregnancy Outcome Prediction [C] . Yuwei Hang, Yan Zhang, Yan Lv, International Conference on Signal Processing Systems . 2020

机译：基于电子医疗记录的不良妊娠结局预测的机器学习方法
5. Sampling Designs for Resource Efficient Collection of Outcome Labels for Machine-Learning, with Application to Electronic Medical Records [D] . Tan, Wei Ling Katherine. 2018

机译：资源高效收集用于机器学习的结果标签的采样设计，并应用于电子病历
6. Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records [O] . Zubair Afzal, Martijn J Schuemie, Jan C van Blijderveen, 2013

机译：从自动文本电子病历中自动识别病例的机器学习方法的敏感性提高
7. Data Leakage in Health Outcomes Prediction With Machine Learning. Comment on “Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning” [O] . Alexandre Chiavegatto Filho, André Filipe De Moraes Batista, Hellen Geremias dos Santos 2021

机译：与机器学习的健康结果预测数据泄漏。评论“明年内发生的事故高压措施：使用全州电子健康记录和机器学习的前瞻性研究”

PRM95 - DETECTING INCIDENTS OF INJECTION FROM ELECTRONIC MEDICAL RECORDS USING MACHINE LEARNING METHODS

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