Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system.
Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system.
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为哮喘研究提取主要诊断、并发症和吸烟状况：对自然语言处理系统的评估。
DOI：
10.1186/1472-6947-6-30
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发表时间：
2006-07-26
影响因子：
3.5
通讯作者：
Lazarus, Ross
中科院分区：
文献类型：
--
作者：
Zeng, Qing T;Goryachev, Sergey;Lazarus, Ross
背景：电子病历中的文字描述是一个丰富的信息源。我们开发了一个健康信息文本提取（HITEX）工具，并使用它来提取气道疾病研究的关键发现。方法：将HITEX从一组150份出院摘要中提取的主要诊断、共病和吸烟状态与专家生成的金标准进行比较。当排除金标准标记为“数据不足”的病例时，HITEX的主要诊断准确率为82%，共病准确率为87%，吸烟状态提取准确率为90%。鉴于出院摘要和提取任务的复杂性，我们认为结果是有希望的。
BACKGROUND: The text descriptions in electronic medical records are a rich source of information. We have developed a Health Information Text Extraction (HITEx) tool and used it to extract key findings for a research study on airways disease.METHODS: The principal diagnosis, co-morbidity and smoking status extracted by HITEx from a set of 150 discharge summaries were compared to an expert-generated gold standard.RESULTS: The accuracy of HITEx was 82% for principal diagnosis, 87% for co-morbidity, and 90% for smoking status extraction, when cases labeled "Insufficient Data" by the gold standard were excluded.CONCLUSION: We consider the results promising, given the complexity of the discharge summaries and the extraction tasks.