用AI自动分析电子病历，识别自杀风险评估中的关键信息
AI-assisted summary of suicide risk Formulation
- 用NLP技术统一临床自由文本，提取自杀风险相关概念
- 通过加权评分获得置信度，准确识别病历中关键风险信息
- 适合精神卫生、临床决策支持系统开发者参考
背景：以自杀风险评估为基础的临床构念（formulation）是个体化理解个体问题及其发展过程的复杂流程。在电子健康记录（EHR）中审计临床文档极具挑战性，因需大量人力手动识别特定表单中的关键词。此外，临床人员使用的术语差异大，常含专业缩写和行话，且相关信息可能分散于不同位置。本研究开发了先进的自然语言处理（NLP）算法，实现对EHR数据的自动化分析。方法：采用先进的光学字符识别（OCR）技术处理非结构化数据（如PDF文件），利用自由文本归一化技术清洗预处理文本。我们构建算法与工具统一自由文本表达，并基于语义匹配技术，使用相似度比对判断构念中各概念是否存在。结果：成功提取与构念相关的指标并评估其覆盖程度，采用加权评分法生成置信度水平。结论：构念完成的严谨性对有效利用EHR至关重要，可确保及时、准确地识别、介入潜在自杀风险，从而避免许多自杀未遂和自杀事件。
原文摘要 · Abstract (English)
Background: Formulation, associated with suicide risk assessment, is an individualised process that seeks to understand the idiosyncratic nature and development of an individual's problems. Auditing clinical documentation on an electronic health record (EHR) is challenging as it requires resource-intensive manual efforts to identify keywords in relevant sections of specific forms. Furthermore, clinicians and healthcare professionals often do not use keywords; their clinical language can vary greatly and may contain various jargon and acronyms. Also, the relevant information may be recorded elsewhere. This study describes how we developed advanced Natural Language Processing (NLP) algorithms, a branch of Artificial Intelligence (AI), to analyse EHR data automatically. Method: Advanced Optical Character Recognition techniques were used to process unstructured data sets, such as portable document format (pdf) files. Free text data was cleaned and pre-processed using Normalisation of Free Text techniques. We developed algorithms and tools to unify the free text. Finally, the formulation was checked for the presence of each concept based on similarity using NLP-powered semantic matching techniques. Results: We extracted information indicative of formulation and assessed it to cover the relevant concepts. This was achieved using a Weighted Score to obtain a Confidence Level. Conclusion: The rigour to which formulation is completed is crucial to effectively using EHRs, ensuring correct and timely identification, engagement and interventions that may potentially avoid many suicide attempts and suicides.
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