Reviewing a Decade of Research Into Suicide and Related Behaviour Using the South London and Maudsley NHS Foundation Trust Clinical Record Interactive Search (CRIS) System.
Reviewing a Decade of Research Into Suicide and Related Behaviour Using the South London and Maudsley NHS Foundation Trust Clinical Record Interactive Search (CRIS) System.
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DOI：
10.3389/fpsyt.2020.553463
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发表时间：
2020
影响因子：
4.7
通讯作者：
Dutta R
中科院分区：
文献类型：
--
作者：
Bittar A;Velupillai S;Downs J;Sedgwick R;Dutta R
自杀是世界范围内一个严重的公共卫生问题，但目前评估一个人自杀风险的临床方法仍然不可靠，并且正在探索评估自杀风险的新方法。电子健康记录 (EHR) 的广泛采用为接受医疗保健的人的自杀及相关行为的流行病学研究开辟了新的可能性。这些类型的记录捕获医疗保健从业者在护理时输入的有价值的信息。然而，最近的许多工作在很大程度上依赖于电子病历的结构化数据，而有关患者护理路径的许多重要信息都记录在临床记录的非结构化文本中。访问和构建用于临床研究（尤其是自杀和自残研究）的文本数据是一项重大挑战，越来越多地使用自然语言处理 (NLP) 和机器学习 (ML) 领域的方法来解决这一挑战。在这篇综述中，我们概述了使用临床记录交互式搜索 (CRIS) 进行的一系列自杀相关研究：这是一个流行病学和临床研究数据库，包含来自南伦敦和莫兹利 NHS 基金会信托基金的去识别化 EHR。我们重点介绍使用 CRIS 探索自杀和相关行为研究的各种临床研究问题、队列和技术，包括 NLP 和 ML 方法的开发。我们展示了 EHR 数据如何提供全面的材料来研究临床人群自杀和自残的患病率。仅有结构化数据是不够的，需要 NLP 方法来更准确地从 EHR 数据中识别相关信息。我们还展示了临床笔记中的文本如何为自杀风险评估的机器学习方法提供信号。我们预计未来几十年将取得更大进展，特别是在多个地点和国家的外部验证研究结果方面，无论是在临床证据方面，还是在 NLP 和机器学习方法可转移性方面。
Suicide is a serious public health issue worldwide, yet current clinical methods for assessing a person's risk of taking their own life remain unreliable and new methods for assessing suicide risk are being explored. The widespread adoption of electronic health records (EHRs) has opened up new possibilities for epidemiological studies of suicide and related behaviour amongst those receiving healthcare. These types of records capture valuable information entered by healthcare practitioners at the point of care. However, much recent work has relied heavily on the structured data of EHRs, whilst much of the important information about a patient's care pathway is recorded in the unstructured text of clinical notes. Accessing and structuring text data for use in clinical research, and particularly for suicide and self-harm research, is a significant challenge that is increasingly being addressed using methods from the fields of natural language processing (NLP) and machine learning (ML). In this review, we provide an overview of the range of suicide-related studies that have been carried out using the Clinical Records Interactive Search (CRIS): a database for epidemiological and clinical research that contains de-identified EHRs from the South London and Maudsley NHS Foundation Trust. We highlight the variety of clinical research questions, cohorts and techniques that have been explored for suicide and related behaviour research using CRIS, including the development of NLP and ML approaches. We demonstrate how EHR data provides comprehensive material to study prevalence of suicide and self-harm in clinical populations. Structured data alone is insufficient and NLP methods are needed to more accurately identify relevant information from EHR data. We also show how the text in clinical notes provide signals for ML approaches to suicide risk assessment. We envision increased progress in the decades to come, particularly in externally validating findings across multiple sites and countries, both in terms of clinical evidence and in terms of NLP and machine learning method transferability.
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影响因子：
4.3
作者：
Cunningham H;Tablan V;Roberts A;Bontcheva K
通讯作者：
Bontcheva K
影响因子：
3.7
作者：
Hayes, Richard D.;Chang, Chin-Kuo;Stewart, Robert
通讯作者：
Stewart, Robert
影响因子：
4.6
作者：
Fernandes AC;Dutta R;Velupillai S;Sanyal J;Stewart R;Chandran D
通讯作者：
Chandran D
影响因子：
6.6
作者：
Hayes, Richard D.;Downs, Johnny;Stewart, Robert
通讯作者：
Stewart, Robert
影响因子：
--
作者：
Dutta, Rina;Murray, Robin M.;Boydell, Jane
通讯作者：
Boydell, Jane