Using artificial intelligence to identify patients with migraine and associated symptoms and conditions within electronic health records.
Using artificial intelligence to identify patients with migraine and associated symptoms and conditions within electronic health records.
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DOI：
10.1186/s12911-023-02190-8
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发表时间：
2023-07-14
影响因子：
3.5
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中科院分区：
文献类型：
--
作者：
真实世界证据（RWE）-基于从电子健康记录（EHR）、索赔和账单数据库、产品和疾病登记以及个人设备和健康应用程序等来源获得的信息-越来越多地用于支持医疗保健决策。在EHR数据的收集中存在可变性，其包括预定义字段中的“结构化数据”（例如，问题列表、未决索赔、药物列表等）和“非结构化数据”作为自由文本或叙述。医疗保健提供者可能会以自由文本的形式提供更完整的信息，但从这些字段中提取意义需要更新的技术和严格的方法来生成更高质量的证据。在此，开发了一种识别与偏头痛的存在和进展相关的概念的方法，并使用EHR数据中的完整患者记录进行了验证，包括结构化和非结构化部分。“传统RWE”方法（即，从结构化EHR字段捕获并使用结构化查询提取）和“高级RWE”方法（即，从非结构化EHR数据中捕获并通过人工智能[AI]技术进行处理，包括自然语言处理和基于AI的推理），并针对从三级护理环境中收集的数据的手动图表抽象参考标准进行了评估。主要终点是回忆;使用卡方比较差异。与手工图表提取相比，传统RWE对偏头痛和头痛的回忆率分别为66.6%和29.6%，高级RWE为96.8%和92.9%;差异具有统计学意义（绝对差异，30.2%和63.3%; P < 0.001）。6种偏头痛相关症状的回忆在更大程度上有利于高级RWE而不是传统RWE（绝对差异，71.5-88.8%; P < 0.001）。传统和先进技术在回忆偏头痛药物方面的差异不太明显，传统RWE约为80%，先进RWE ≥ 98%（P < 0.001）。使用人工智能技术处理的非结构化EHR数据提供了一种比单独使用结构化EHR和索赔数据更可靠的方法，使RWE能够治疗偏头痛。开发了一种算法，可用于进一步研究和验证RWE的使用，以支持偏头痛患者的诊断和管理。在线版本包含补充材料，可通过10.1186/s12911-023-02190-8获得。
Real-world evidence (RWE)—based on information obtained from sources such as electronic health records (EHRs), claims and billing databases, product and disease registries, and personal devices and health applications—is increasingly used to support healthcare decision making. There is variability in the collection of EHR data, which includes “structured data” in predefined fields (e.g., problem list, open claims, medication list, etc.) and “unstructured data” as free text or narrative. Healthcare providers are likely to provide more complete information as free text, but extracting meaning from these fields requires newer technologies and a rigorous methodology to generate higher-quality evidence. Herein, an approach to identify concepts associated with the presence and progression of migraine was developed and validated using the complete patient record in EHR data, including both the structured and unstructured portions. “Traditional RWE” approaches (i.e., capture from structured EHR fields and extraction using structured queries) and “Advanced RWE” approaches (i.e., capture from unstructured EHR data and processing by artificial intelligence [AI] technology, including natural language processing and AI-based inference) were evaluated against a manual chart abstraction reference standard for data collected from a tertiary care setting. The primary endpoint was recall; differences were compared using chi square. Compared with manual chart abstraction, recall for migraine and headache were 66.6% and 29.6%, respectively, for Traditional RWE, and 96.8% and 92.9% for Advanced RWE; differences were statistically significant (absolute differences, 30.2% and 63.3%; P < 0.001). Recall of 6 migraine-associated symptoms favored Advanced RWE over Traditional RWE to a greater extent (absolute differences, 71.5–88.8%; P < 0.001). The difference between traditional and advanced techniques for recall of migraine medications was less pronounced, approximately 80% for Traditional RWE and ≥ 98% for Advanced RWE (P < 0.001). Unstructured EHR data, processed using AI technologies, provides a more credible approach to enable RWE in migraine than using structured EHR and claims data alone. An algorithm was developed that could be used to further study and validate the use of RWE to support diagnosis and management of patients with migraine. The online version contains supplementary material available at 10.1186/s12911-023-02190-8.
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DOI：
10.1016/s1474-4422(18)30387-9
发表时间：
2018-12
期刊：
The Lancet. Neurology
影响因子：
--
作者：
GBD 2016 Meningitis Collaborators
通讯作者：
GBD 2016 Meningitis Collaborators
影响因子：
6.7
作者：
Miksad RA;Abernethy AP
通讯作者：
Abernethy AP
DOI：
10.1136/amiajnl-2011-000557
发表时间：
2012-07
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Parsons A;McCullough C;Wang J;Shih S
通讯作者：
Shih S
影响因子：
2.5
作者：
Kennedy-Martin T;Curtis S;Faries D;Robinson S;Johnston J
通讯作者：
Johnston J
影响因子：
3.8
作者：
Blonde L;Khunti K;Harris SB;Meizinger C;Skolnik NS
通讯作者：
Skolnik NS