Birth defects research. 2025 Mar;117(3):e2451. doi: 10.1002/bdr2.2451 Q31.62025

Machine Learning and Natural Language Processing to Improve Classification of Atrial Septal Defects in Electronic Health Records

利用机器学习和自然语言处理改进电子健康记录中房间隔缺损的分类翻译改进

Yuting Guo¹, Haoming Shi², Wendy M Book^{3 4}, Lindsey Carrie Ivey⁴, Fred H Rodriguez 3rd³, Reza Sameni¹, Cheryl Raskind-Hood⁴, Chad Robichaux¹, Karrie F Downing⁵, Abeed Sarker¹

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作者单位

¹ Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA.

² Department of Biomedical Engineering, Georgia Institute Technology, Atlanta, Georgia, USA.

³ Department of Cardiology, School of Medicine, Emory University, Atlanta, Georgia, USA.

⁴ Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.

⁵ National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

DOI: 10.1002/bdr2.2451 PMID: 40035168

摘要 Ai翻译

Background: International Classification of Disease (ICD) codes can accurately identify patients with certain congenital heart defects (CHDs). In ICD-defined CHD data sets, the code for secundum atrial septal defect (ASD) is the most common, but it has a low positive predictive value for CHD, potentially resulting in the drawing of erroneous conclusions from such data sets. Methods with reduced false positive rates for CHD am... ...点击完成人机验证后继续浏览

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期刊名：Birth defects research

缩写：BIRTH DEFECTS RES

ISSN：2472-1727

e-ISSN：2472-1727

IF/分区：1.6/Q3

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Machine Learning and Natural Language Processing to Improve Classification of Atrial Septal Defects in Electronic Health Records

利用机器学习和自然语言处理改进电子健康记录中房间隔缺损的分类翻译改进

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

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

基于自然语言处理、机器学习和患者电子健康记录的联合药物治疗预测与评估

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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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

Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records

使用压力灌注心脏磁共振报告和电子健康记录的自然语言处理进行机器学习结果预测

Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population

利用知识工程、自然语言处理和机器学习对大型电子健康记录人群进行芳香族l-氨基酸脱羧酶缺乏症的自动患者预筛查

Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation

基于电子健康记录进行中风识别的自然语言处理和机器学习算法的研发与验证

Correction: Extracting Family History Information From Electronic Health Records: Natural Language Processing Analysis

关于从电子健康记录中提取家族史信息的自然语言处理分析的更正通知

Machine Learning and Natural Language Processing to Improve Classification of Atrial Septal Defects in Electronic Health Records

利用机器学习和自然语言处理改进电子健康记录中房间隔缺损的分类 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

基于自然语言处理、机器学习和患者电子健康记录的联合药物治疗预测与评估

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

基于自然语言处理和机器学习的电子健康记录精神病患病预测模型设计规范

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

Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records

使用压力灌注心脏磁共振报告和电子健康记录的自然语言处理进行机器学习结果预测

Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population

利用知识工程、自然语言处理和机器学习对大型电子健康记录人群进行芳香族l-氨基酸脱羧酶缺乏症的自动患者预筛查

Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation

基于电子健康记录进行中风识别的自然语言处理和机器学习算法的研发与验证

Correction: Extracting Family History Information From Electronic Health Records: Natural Language Processing Analysis

关于从电子健康记录中提取家族史信息的自然语言处理分析的更正通知

利用机器学习和自然语言处理改进电子健康记录中房间隔缺损的分类翻译改进