JAMIA open. 2021 Mar 17;4(1):ooab011. doi: 10.1093/jamiaopen/ooab011 Q13.42025

Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts

基于电子健康档案的自然语言处理和机器学习预测首次自杀尝试事件翻译改进

Fuchiang R Tsui^{1 2 3 4}, Lingyun Shi^{1 3}, Victor Ruiz^{1 3}, Neal D Ryan⁵, Candice Biernesser⁵, Satish Iyengar⁶, Colin G Walsh⁷, David A Brent⁵

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作者单位

¹ Tsui Laboratory, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.

² Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.

³ Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.

⁴ Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

⁵ Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

⁶ Department of Statistics, School of Arts and Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

⁷ Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, Tennessee, USA.

DOI: 10.1093/jamiaopen/ooab011 PMID: 33758800

摘要 Ai翻译

Objective: Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data. ... ...点击完成人机验证后继续浏览

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期刊名：Jamia open

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e-ISSN：2574-2531

IF/分区：3.4/Q1

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Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts

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基于电子健康档案的自然语言处理和机器学习预测首次自杀尝试事件 翻译改进

Predictive model for a second hip fracture occurrence using natural language processing and machine learning on electronic health records

使用自然语言处理和机器学习对电子健康记录预测第二次股骨骨折的发生模型

Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records

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Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural Language Processing and Machine Learning

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基于电子健康档案的自然语言处理和机器学习预测首次自杀尝试事件翻译改进