Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study

Sahni Nishant; Simon Gyorgy; Arora Rashi

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Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study

机译：在多态患者住院期间利用电子医疗记录数据的1年死亡率预测机器学习模型的开发和验证：概念验证研究

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Predicting death in a cohort of clinically diverse, multicondition hospitalized patients is difficult. Prognostic models that use electronic medical record (EMR) data to determine 1-year death risk can improve end-of-life planning and risk adjustment for research.

机译：预测临床各种各样的多种多样，多功能住院患者的死亡是困难的。使用电子医疗记录（EMR）数据来确定1年死亡风险的预后模型可以改善生命终止规划和研究风险调整。

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来源
《Journal of general internal medicine》 |2018年第6期|共8页
作者
Sahni Nishant; Simon Gyorgy; Arora Rashi;
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作者单位

Univ Minnesota Div Gen Internal Med Minneapolis MN 55455 USA;

Univ Minnesota Inst Hlth Informat Minneapolis MN USA;

Univ Minnesota Div Gen Internal Med Minneapolis MN 55455 USA;

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原文格式 PDF
正文语种 eng
中图分类内科学;
关键词
machine learning; hospital outcomes; predictive models; data mining;

机译：机器学习;医院结果;预测模型;数据挖掘;

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1. Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study [J] . Sahni Nishant, Simon Gyorgy, Arora Rashi Journal of general internal medicine . 2018,第6期

机译：在多态患者住院期间利用电子医疗记录数据的1年死亡率预测机器学习模型的开发和验证：概念验证研究
2. Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study [J] . Kristin M. Corey, Sehj Kashyap, Elizabeth Lorenzi, PLoS Medicine . 2018,第11期

机译：使用自动整理的电子健康记录数据（Pythia）开发和验证机器学习模型，以识别高危手术患者：回顾性，单点研究
3. Electronic Health Record Mortality Prediction Model for Targeted Palliative Care Among Hospitalized Medical Patients: a Pilot Quasi-experimental Study [J] . Courtright Katherine R., Chivers Corey, Becker Michael, Journal of general internal medicine . 2019,第9期

机译：住院医疗患者有针对性姑息治疗的电子健康记录死亡率预测模型：试验准实验研究
4. Using Electronic Health Records and Machine Learning to Make Medical-Related Predictions from Non-Medical Data [C] . Stavros Pitoglou, Yiannis Koumpouros, Athanasios Anastasiou International Conference on Machine Learning and Data Engineering . 2018

机译：使用电子病历和机器学习根据非医学数据做出医学相关预测
5. Electronic medical records (EMR) adoption, patient mortality, and patient safety in U.S. hospitals: A longitudinal study (2008-2014) [D] . Hosfield, Jerilyn. 2017

机译：美国医院采用电子病历（EMR），患者死亡率和患者安全性：一项纵向研究（2008-2014）
6. Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study [O] . Nishant Sahni, Gyorgy Simon, Rashi Arora 2018

机译：利用多病患者住院末期可用的电子病历数据预测和预测一年死亡率的机器学习模型：概念验证研究
7. Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study [O] . Chien-Ning Hsu, Chien-Liang Liu, You-Lin Tain, 2020

机译：电子卫生记录社区急性肾脏损伤住院风险预测机器学习模型：开发与验证研究

Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study

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