Text mining occupations from the mental health electronic health record: a natural language processing approach using records from the Clinical Record Interactive Search (CRIS) platform in south London, UK.
Text mining occupations from the mental health electronic health record: a natural language processing approach using records from the Clinical Record Interactive Search (CRIS) platform in south London, UK.
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DOI：
10.1136/bmjopen-2020-042274
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发表时间：
2021-03-25
期刊：
影响因子：
2.9
通讯作者：
Das-Munshi J
中科院分区：
文献类型：
--
作者：
Chilman N;Song X;Roberts A;Tolani E;Stewart R;Chui Z;Birnie K;Harber-Aschan L;Gazard B;Chandran D;Sanyal J;Hatch S;Kolliakou A;Das-Munshi J
我们着手开发、评估和实现一个新的应用程序，使用自然语言处理从精神病临床笔记的自由文本中挖掘职业。使用文本工程通用架构软件开发和验证自然语言处理应用程序，从去识别的临床记录中提取职业。来自伦敦南部一家大型二级精神卫生保健提供商的电子健康记录，通过临床记录交互式搜索平台访问。文本挖掘应用程序在341720例患者（年龄均≥16岁）的电子健康记录中的自由文本字段上运行。应用程序性能的精确度和召回率估计;使用应用程序与结构化字段进行职业检索;最常见的患者职业;以及职业记录的关键社会人口统计学和临床指标分析。单独使用结构化字段，只有14%的患者有职业记录。除了结构化字段之外，通过实施文本挖掘应用程序，57%的患者的职业被确定。该应用程序在黄金标准的人类注释的临床文本上执行，精确度为0.79，召回率为0.77。最常见的患者职业记录为“学生”和“失业”。有更多服务接触的患者更有可能有职业记录，男性患者、老年患者和生活在贫困程度较低地区的患者也是如此。这是第一次使用自然语言处理应用程序成功地从电子精神健康记录的自由文本中获得患者级别的职业，具有良好的精确度和召回率，并大规模应用。这可用于为使用电子健康记录的更广泛的健康社会决定因素相关的临床研究提供信息。
We set out to develop, evaluate and implement a novel application using natural language processing to text mine occupations from the free-text of psychiatric clinical notes. Development and validation of a natural language processing application using General Architecture for Text Engineering software to extract occupations from de-identified clinical records. Electronic health records from a large secondary mental healthcare provider in south London, accessed through the Clinical Record Interactive Search platform. The text mining application was run over the free-text fields in the electronic health records of 341 720 patients (all aged ≥16 years). Precision and recall estimates of the application performance; occupation retrieval using the application compared with structured fields; most common patient occupations; and analysis of key sociodemographic and clinical indicators for occupation recording. Using the structured fields alone, only 14% of patients had occupation recorded. By implementing the text mining application in addition to the structured fields, occupations were identified in 57% of patients. The application performed on gold-standard human-annotated clinical text at a precision level of 0.79 and recall level of 0.77. The most common patient occupations recorded were ‘student’ and ‘unemployed’. Patients with more service contact were more likely to have an occupation recorded, as were patients of a male gender, older age and those living in areas of lower deprivation. This is the first time a natural language processing application has been used to successfully derive patient-level occupations from the free-text of electronic mental health records, performing with good levels of precision and recall, and applied at scale. This may be used to inform clinical studies relating to the broader social determinants of health using electronic health records.
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